Contrastive learning has strong generalization ability and the capability to learn automatically without labeled information. However, it still faces challenges such as insufficient feature diversity, a lack of multi-level semantics, and the balance between tolerance and consistency. To address these challenges, This study propose a Multi-view Hierarchical Graph Contrastive Learning method. First, a new view is generated through a diffusion matrix to provide multi-view data for contrastive learning. Then, these multi-view data are fed into an asynchronous asymmetric network structure, specifically using graph network models to learn diversified features. Next, we adopt a self-designed hierarchical contrastive learning framework, constructing a three-level contrastive loss for joint optimization of nodes, subgraphs, and global graphs. Meanwhile, we introduce alignment and consistency and appropriately adjust the loss function through a temperature coefficient. Ultimately, the model achieves excellent classification performance on multiple datasets through node classification and graph classification tasks.

Graph Neural Networks fundamentally rely on homophily assumptions where connected nodes are expected to share similar labels, consequently suffering severe performance degradation in heterophilic graphs due to the indiscriminate neighbor aggregation mechanism. Although recent solutions have attempted to incorporate higher-order neighborhoods or reweighting schemes, they often inadvertently amplify structural noise by introducing a larger proportion of dissimilar nodes than similar ones, while simultaneously failing to capture nuanced contextual patterns due to their inability to discern subtle local structural variations across subgraphs. To holistically address these intractable and co-existing challenges, we propose the Contextual Structure Enhanced Selective Graph Convolutional Network (CSS-GCN), a novel architecture that organically synergizes contextual structure modeling with adaptive neighbor selection. Specifically, our approach employs ego-network partitioning and group fairness constraints to effectively quantify domain-invariant structural patterns, thereby countering the contextual blindness often observed in conventional GNNs. Complementarily, we design a selective propagation mechanism unifying adaptive neighborhood distribution-based similarity computation with the gated fusion of three distinct information pathways: potential homophilic neighbors identified through attribute-topology synergy, first-hop connections, and ego-representations. This dual-component framework enables nodes to dynamically filter out irrelevant signals while preserving structural consistency across diverse homophily-heterophily landscapes. Extensive validation on 10 real-world graphs demonstrates the effectiveness and superiority of our proposed approach.

Current contrastive graph clustering approaches suffer from insufficient integration of structural and semantic information, coupled with the absence of reliable sample selection strategies. To address these dual limitations, we introduce Structure-Semantic Synergized Deep Contrastive Graph Clustering (S³-DCGC), a novel framework that jointly models topological structure and semantic features through two synergistic mechanisms. First, a structure-aware curriculum negative sampling strategy progressively identifies hard negative samples using dynamic-range masking, enhancing discriminative structural learning. Second, a semantic confidence-guided contrastive mechanism quantifies node reliability via composite confidence scores—integrating cluster affinity and cross-view consistency—to select high-confidence positive/negative pairs. Dynamically coordinated by a soft-alignment strategy that shifts optimization focus from structural to semantic dominance during training, these components achieve balanced synergy. Comprehensive experiments conducted on five benchmark datasets demonstrate S³-DCGC's superiority, achieving significant performance gains. Ablation studies and visual analyses further corroborate the critical importance of structure-semantic synergy in achieving robust clustering performance.

Employing graph neural networks (GNNs) for graph clustering has shown promising results in deep graph clustering (DGC). However, existing methods disregard the reciprocal relationship between representation learning and structure augmentation: the more homogeneous the graph, the more cohesive the node representations; the more cohesive the node representations, the more reliable the structure augmentation becomes. Moreover, the generalization ability of existing GNN-based models on the low homophily graph is relatively poor. To this end, we propose a graph clustering framework named synergistic deep graph clustering network (SynC). SynC employs a transform input graph autoencoder (TIGAE) to obtain high-quality embeddings via mitigating the representation collapse issue of GAE for guiding structure augmentation. Then, we recapture neighborhood representations on the refined graph to obtain clustering-friendly embeddings and conduct self-supervised clustering. Notably, these two stages share weights, resulting in synergistic boosting while significantly reducing the number of model parameters. Additionally, we introduce a structure fine-tuning (SF) strategy to improve the model's generalization on the low homophily graph. Extensive experiments on benchmark datasets demonstrate the superiority of SynC. The code is released at https://github.com/Marigoldwu/SynC.

Recent research in cooperative Multi-Agent Reinforcement Learning (MARL) has shown significant interest in utilizing Graph Neural Networks (GNNs) for communication learning due to their strong ability to process feature and topological information of agents into message representations for downstream action selection and coordination. However, GNNs generally assume network homogeneity that nodes of the same class tend to be interconnected. In real-world multi-agent systems, such assumptions are often unrealistic, as agents within the same class can be distant from each other. Furthermore, GNN-based MARL methods overlook the crucial role of feature similarity of agents in action coordination, which also restricts their performance. To overcome these limitations, we propose a Multi-Agent communication mechanism with Information preserving graph contrastive Learning (MAIL), which enhances message representation by preserving the comprehensive features of adjacent agents while integrating topological information. Specifically, MAIL considers three distinct graph views: original view, agent feature view, and global topological view. MAIL performs contrastive learning across three views to extract comprehensive information. MAIL effectively learns robust and expressive message representations for downstream tasks. Extensive experiments across various environments demonstrate that MAIL outperforms existing GNN-based MARL methods.

Due to variations in imaging conditions, images often exhibit discrepancies in color reproduction. Furthermore, motion-induced blur can lead to edge degradation, making color sensitivity and edge blurriness two prevalent and challenging issues in both natural image processing and medical image analysis. To address these challenges, we propose a model termed the Three-View Consistency Mo-mentum Contrastive with Sobel Operator (SVCMC). Specifically, we first design a three-view momen-tum-update architecture that employs a So-bel-augmented ResNet as the backbone. We then introduce a novel contrastive loss, referred to as the Three-View Consistency Momentum Contrastive Loss. Next, to mitigate the oscillations and slow convergence commonly observed in contrastive learning, we construct a dynamic contrastive loss function that adapts in real time over the training process. Finally, we validated the superiority of our model on two medical image datasets and one natural image dataset, where its classification ac-curacy and convergence speed significantly out-performed existing state-of-the-art contrastive models.

In image denoising research, discriminative models have achieved impressive results which mainly owes to the powerful ability of convolutional networks in local feature extraction. However, there is still room for improvement due to insufficient utilization of global information. Although using fully connected layers or increasing network depth can supplement global information, this results in a significant increase in parameters and computational cost. To address these issues, we propose a global information compensation network (GICN) for image denoising in this paper. Firstly, at the shallow network part, we propose a global feature mining block that enhances the network's ability to extract global information by combining non-local blocks and the Fourier transform while improving the interpretability of the model. Secondly, between the encoder and decoder, we propose a cross-scale feature aggregation block to fuse information at different scales. Finally, we employ attention blocks to improve skip connections to better capture long-distance dependencies. Extensive experimental results show that our proposed GICN effectively compensates for global information, achieves a balance between denoising efficiency and effect, and surpasses mainstream methods in multiple benchmark tests.

Emotion Recognition in Conversation (ERC) aims to detect the emotions of individual utterances within a conversation. Generating efficient and modality-specific representations for each utterance remains a significant challenge. Previous studies have proposed various models to integrate features extracted using different modality-specific encoders. However, they neglect the varying contributions of modalities to this task and introduce high complexity by aligning modalities at the frame level. To address these challenges, we propose the Multi-modal Anchor Gated Transformer with Knowledge Distillation (MAGTKD) for the ERC task. Specifically, prompt learning is employed to enhance textual modality representations, while knowledge distillation is utilized to strengthen representations of weaker modalities. Furthermore, we introduce a multi-modal anchor gated transformer to effectively integrate utterance-level representations across modalities. Extensive experiments on the IEMOCAP and MELD datasets demonstrate the effectiveness of knowledge distillation in enhancing modality representations and achieve state-of-the-art performance in emotion recognition. Our code is available at: https://github.com/JieLi-dd/MAGTKD.

Graph Neural Networks (GNNs) are powerful models for node classification, but their performance is heavily reliant on manually labeled data, which is often costly and results in insufficient labeling. Recent studies have shown that message-passing neural networks struggle to propagate information in low-degree nodes, negatively affecting overall performance. To address the information bias caused by degree imbalance, we propose a Learnable Enhancement and Label Selection Dynamic Graph Convolutional Network (L2DGCN). L2DGCN consists of a teacher model and a student model. The teacher model employs an improved label propagation mechanism that enables remote label information dissemination among all nodes. The student model introduces a dynamically learnable graph enhancement strategy, perturbing edges to facilitate information exchange among low-degree nodes. This approach maintains the global graph structure while learning graph representations. Additionally, we have designed a label selector to mitigate the impact of unreliable pseudo-labels on model learning. To validate the effectiveness of our proposed model with limited labeled data, we conducted comprehensive evaluations of semi-supervised node classification across various scenarios with a limited number of annotated nodes. Experimental results demonstrate that our data enhancement model significantly contributes to node classification tasks under sparse labeling conditions.

Density peaks clustering (DPC) is an excellent clustering algorithm that does not need any prior knowledge. However, DPC still has the following shortcomings: (1) The Euclidean distance used by it is not applicable to manifold data with multiple peaks. (2) The local density calculation for DPC is too simple, and the final results may fluctuate due to the cutoff-distancedc. (3) Manually selected centers by decision-graph may lead to a wrong number of clusters and poor performance. To address these shortcomings and improve the performance, a robust density peaks clustering algorithm for manifold data with multiple peaks (RDPCM) is proposed to reduce the sensitivity of clustering results to parameters. Motivated by DPC-GD, RDPCM replaces the Euclidean distance with geodesic distance, which is optimized by the improved mutual K-nearest neighbors. It better considers the local manifold structure of the datasets and obtains excellent results. In addition, the Davies-Bouldin Index based on Minimum Spanning Tree (MDBI) is proposed to select the ideal number of classes adaptively. Numerous experiments have established that RDPCM is more effective and superior than other advanced clustering algorithms.

Since the regulatory roles of microRNAs in complex human diseases have been gradually demonstrated, more and more enlightening models were developed for predicting microRNA-disease associations. These models can inform bio-logical studies on the differential expression of microRNAs. In this paper, a graph convolutional network-based model using multi-view feature fusion and matrix completion, FMCGCN for brevity, is proposed. First of all, graph autoencoders are used to learn multiple embeddings from different networks. For convenience, attention mechanisms are used to fuse them. As a result, multi-view features constructed from different perspectives are aggregated. Then, the fused embedding is fed into the graph convolutional network to aggregate local information. This fused embedding is thought to facilitate feature extraction for graph convolutional networks. Finally, feature and nuclear norm minimization, a method for matrix completion, is used to obtain the prediction matrix. Additionally, evaluation results under 5-fold cross-validation prove that FMCGCN outperforms current models in several metrics. Furthermore, case studies for esophageal neoplasms and pancreatic neoplasms also demonstrate the validity of our model.

Density peaks clustering (DPC) is one of the density-based clustering algorithms and has been widely studied and applied in recent years because of its unique parameter, non-iteration and good robustness. However, it cannot effectively identify the cluster centers, and time and space complexities are too high. To this end, this paper proposes a fast density peaks clustering algorithm based on approximatek-nearest neighbors (FDPAN). Firstly, it uses Balanced K-means based Hierarchical K-means (BKHK) method to partition the data and quickly find the approximatek-nearest neighbors (AKNN), improving the algorithm’s efficiency on large-scale high-dimensional data. Meanwhile, three-way clustering is used to improve the neighbor search of the boundary points of the partition. Then, the local density and relative distance of DPC are recalculated by AKNN. Finally, according to the similar density chain, the connected high-density points are labeled while searching for the cluster center, and the remaining points are assigned to the clusters where their nearest higher-density points are located. Theoretical analysis and experiments on synthetic and real datasets show that FDPAN can obtain higher clustering results and shorten the operation time on large-scale high-dimensional data compared with DPC and its variants.

As the representative density-based clustering algorithm, density peaks clustering (DPC) has wide recognition, and many improved algorithms and applications have been extended from it. However, the DPC involving privacy protection has not been deeply studied. In addition, there is still room for improvement in the selection of centers and allocation methods of DPC. To address these issues, vertical federated density peaks clustering under nonlinear mapping (VFDPC) is proposed to address privacy protection issues in vertically partitioned data. Firstly, a hybrid encryption privacy protection mechanism is proposed to protect the merging process of distance matrices generated by client data. Secondly, according to the merged distance matrix, a more effective cluster merging under nonlinear mapping is proposed to ameliorate the process of DPC. Results on man-made, real, and multi-view data fully prove the improvement of VFDPC on clustering accuracy.

Most clustering algorithms require setting one or more parameters, which rely on prior knowledge or are constantly adjusted based on external indicators. To address the issues of requiring external index guidance, blindness, and time-consuming parameter setting for clustering algorithms on complex data, we propose a novel Parameter-Adaptive Border Peeling clustering algorithm (PABP). The PABP algorithm initially employs the maximum number of neighbors identified through natural neighbor search to automatically ascertain the number of local neighborhoods. At the same time, the Gaussian kernel bandwidth can be adaptively obtained in density measurement, which can highlight high-density areas. Secondly, the number of peels is adaptively determined by the coefficient of variation of density during the iterative border peeling process. Lastly, labels are assigned to core points based on graph connections, while the clustering of border points is accomplished via label propagation. PABP does not require users to adjust parameters based on prior knowledge or external indicators throughout the entire process. In the experiment, PABP was compared with seven other advanced clustering algorithms on 13 synthetic datasets, 10 UCI datasets, and Olivetti Face and MNIST datasets. The results indicate that the clustering performance of PABP is superior to the compared algorithms.

Communication learning is an important research direction in the multiagent reinforcement learning (MARL) domain. Graph neural networks (GNNs) can aggregate the information of neighbor nodes for representation learning. In recent years, several MARL methods leverage GNN to model information interactions between agents to coordinate actions and complete cooperative tasks. However, simply aggregating the information of neighboring agents through GNNs may not extract enough useful information, and the topological relationship information is ignored. To tackle this difficulty, we investigate how to efficiently extract and utilize the rich information of neighbor agents as much as possible in the graph structure, so as to obtain high-quality expressive feature representation to complete the cooperation task. To this end, we present a novel GNN-based MARL method with graphical mutual information (MI) maximization to maximize the correlation between input feature information of neighbor agents and output high-level hidden feature representations. The proposed method extends the traditional idea of MI optimization from graph domain to multiagent system, in which the MI is measured from two aspects: agent features information and agent topological relationships. The proposed method is agnostic to specific MARL methods and can be flexibly integrated with various value function decomposition methods. Considerable experiments on various benchmarks demonstrate that the performance of our proposed method is superior to the existing MARL methods.

Density peaks clustering (DPC) is a popular clustering algorithm, which has been studied and favored by many scholars because of its simplicity, fewer parameters, and no iteration. However, in previous improvements of DPC, the issue of privacy data leakage was not considered, and the "Domino" effect caused by the misallocation of noncenters has not been effectively addressed. In view of the above shortcomings, a horizontal federated DPC (HFDPC) is proposed. First, HFDPC introduces the idea of horizontal federated learning and proposes a protection mechanism for client parameter transmission. Second, DPC is improved by using similar density chain (SDC) to alleviate the "Domino" effect caused by multiple local peaks in the flow pattern dataset. Finally, a novel data dimension reduction and image encryption are used to improve the effectiveness of data partitioning. The experimental results show that compared with DPC and some of its improvements, HFDPC has a certain degree of improvement in accuracy and speed.

Speech emotion recognition (SER) plays an important role in human-computer interaction, which can provide better interactivity to enhance user experiences. Existing approaches tend to directly apply deep learning networks to distinguish emotions. Among them, the convolutional neural network (CNN) is the most commonly used method to learn emotional representations from spectrograms. However, CNN does not explicitly model features' associations in the spectral-, temporal-, and channel-wise axes or their relative relevance, which will limit the representation learning. In this article, we propose a deep spectro-temporal-channel network (DSTCNet) to improve the representational ability for speech emotion. The proposed DSTCNet integrates several spectro-temporal-channel (STC) attention modules into a general CNN. Specifically, we propose the STC module that infers a 3-D attention map along the dimensions of time, frequency, and channel. The STC attention can focus more on the regions of crucial time frames, frequency ranges, and feature channels. Finally, experiments were conducted on the Berlin emotional database (EmoDB) and interactive emotional dyadic motion capture (IEMOCAP) databases. The results reveal that our DSTCNet can outperform the traditional CNN-based and several state-of-the-art methods.

Stochastic configuration network (SCN) is a kind of incremental random neural network that assigns input weights and biases through data-dependent supervisory mechanism. However, the robustness of SCN is significantly reduced when processing the data disturbed by outliers. Aiming at improve the noisy data regression performance of SCN, this article presents a novel robust decorrelated SCNs ensemble model (RDSCNE). Such a robust decorrelated ensemble framework adopts weighted negative correlation learning (WNCL) and a robust regularization technique, which can guarantee the generalization performance for noisy data processing. Specifically, we first present a WNCL framework based on kernel density estimation (KDE) to build SCNs ensemble model, so that the negative effects of noise can be suppressed through KDE to calculate penalty weights of each training sample for the computation of ensemble weights. Meanwhile,l1norm loss function combined withl2regularization technique is employed as the objective function of base components. This approach is designed to process outliers with sparse characteristics and alleviate the over-fitting phenomenon. Then, augmented Lagrange multiplier (ALM) method is used to calculate the objective function. Experimental results over some regression datasets with Gaussian outliers demonstrate that the proposed RDSCNE model has better robustness than the various SCN variants.

Efficient communication learning among agents has been shown crucial for cooperative multi-agent reinforcement learning (MARL), as it can promote the action coordination of agents and ultimately improve performance. Graph neural network (GNN) provide a general paradigm for communication learning, which consider agents and communication channels as nodes and edges in a graph, with the action selection corresponding to node labeling. Under such paradigm, an agent aggregates information from neighbor agents, which can reduce uncertainty in local decision-making and induce implicit action coordination. However, this communication paradigm is vulnerable to adversarial attacks and noise, and how to learn robust and efficient communication under perturbations has largely not been studied. To this end, this paper introduces a novel Multi-Agent communication mechanism via Graph Information bottleneck (MAGI), which can optimally balance the robustness and expressiveness of the message representation learned by agents. This communication mechanism is aim at learning the minimal sufficient message representation for an agent by maximizing the mutual information (MI) between the message representation and the selected action, and simultaneously constraining the MI between the message representation and the agent feature. Empirical results demonstrate that MAGI is more robust and efficient than state-of-the-art GNN-based MARL methods.

Information sharing through communication is essential for tackling complex multi-agent reinforcement learning tasks. Many existing multi-agent communication protocols can be viewed as instances of message passing graph neural networks (GNNs). However, due to the significantly limited expressive ability of the standard GNN method, the agent feature representations remain similar and indistinguishable even though the agents have different neighborhood structures. This further results in the homogenization of agent behaviors and reduces the capability to solve tasks effectively. In this paper, we propose a multi-agent communication protocol via identity-aware learning (IDEAL), which explicitly enhances the distinguishability of agent feature representations to break the diversity bottleneck. Specifically, IDEAL extends existing multi-agent communication protocols by inductively considering the agents' identities during the message passing process. To obtain expressive feature representations for a given agent, IDEAL first extracts the ego network centered around that agent and then performs multiple rounds of heterogeneous message passing, where different parameter sets are applied to the central agent and the other surrounding agents within the ego network. IDEAL fosters expressive communication between agents and generates distinguishable feature representations, which promotes action diversity and individuality emergence. Experimental results on various benchmarks demonstrate IDEAL can be flexibly integrated into various multi-agent communication methods and enhances the corresponding performance.

Recent research on multi-agent reinforcement learning (MARL) has shown that action coordination of multi-agents can be significantly enhanced by introducing communication learning mechanisms. Meanwhile, graph neural network (GNN) provides a promising paradigm for communication learning of MARL. Under this paradigm, agents and communication channels can be regarded as nodes and edges in the graph, and agents can aggregate information from neighboring agents through GNN. However, this GNN-based communication paradigm is susceptible to adversarial attacks and noise perturbations, and how to achieve robust communication learning under perturbations has been largely neglected. To this end, this paper explores this problem and introduces a robust communication learning mechanism with graph information bottleneck optimization, which can optimally realize the robustness and effectiveness of communication learning. We introduce two information-theoretic regularizers to learn the minimal sufficient message representation for multi-agent communication. The regularizers aim at maximizing the mutual information (MI) between the message representation and action selection while minimizing the MI between the agent feature and message representation. Besides, we present a MARL framework that can integrate the proposed communication mechanism with existing value decomposition methods. Experimental results demonstrate that the proposed method is more robust and efficient than state-of-the-art GNN-based MARL methods.

Convolutional neural networks (CNN) have achieved remarkable performance in image denoising. However, most existing CNNs cannot accurately capture and remove tiny noises during the denoising process and lose edge detail information easily. In this paper, we propose a fine-grained residual network guided by wavelet and adaptive coordinate attention (WACAFRN) for image denoising. Firstly, we propose an adaptive coordinate attention mechanism and combine it with cascaded Res2Net residual blocks to form an encoder network for more accurate noise removal. Secondly, we propose a wavelet attention mechanism that combines global and local residual blocks to form a decoder network, aiming to address the problem of edge detail information loss. At last, we complement the noise information through a noise estimation block to further enhance the model’s ability to adapt to noise. Extensive experiment results demonstrate that our proposed method outperforms existing denoising methods in both qualitative and quantitative aspects. Notably, our method significantly improves real-world noise removal tasks on the CC dataset, with an average increase of 2.08 dB in PSNR and 0.0264 in SSIM over the state-of-the-art methods. Additionally, WACAFRN exhibits faster inference speeds, underscoring its efficiency in real-world applications.

Stochastic configuration network (SCN) is an emerging type of random neural network that allocates node parameters via supervised mechanisms to ensure universal approximation capability. However, outliers and noise in realworld data can adversely affect SCN's classification performance. To enhance SCN for binary classification, we introduce intuitionistic fuzzy set concepts to propose intuitionistic fuzzy SCN (IFSCN). Unlike SCN, IFSCN allocates an intuitionistic fuzzy number to every sample by computing degrees of membership and non-membership, thereby crafting an optimal classifier through strategic weighting of samples to mitigate the influence of noise. Furthermore, stochastic configuration sparse autoencoders (SCSAE) effectively learn sparse features using L1 regularization. By integrating multiple SC-SAE models, we extract robust sparse feature representations. We then propose hierarchical IFSCN (IFHSCN) built on SC-SAE for enhanced accuracy. Comprehensive experiments on 8 benchmark datasets demonstrate IFSCN and IFHSCN achieve superior binary classification performance over state-of-the-art models like intuitionistic fuzzy twin SVM, kernel ridge regression, random vector functional link networks. Overall, this study successfully equips SCN for real-world noisy data via intuitionistic fuzzy sets and sparsity, providing an effective and scalable solution for robust classification.

The fuzzy twin support vector machine (FTSVM) is a powerful and effective classifier due to the use of nonparallel hyperplanes and fuzzy membership functions. This article extends the FTSVM model to uncertain objects with probability distributions and proposes a distribution-input FTSVM (DFTSVM) model. Unlike the classical FTSVM, the DFTSVM model adopts an insensitive pinball loss function that can suppress feature noise. It also defines a fuzzy membership function of uncertain objects in terms of the Wasserstein distance andk-nearest neighbors. We use properties of Gaussian distributions to transform the original DFTSVM into a tractable model. When covariance matrices of uncertain objects are positive definite, two optimization problems in DFTSVM are smooth and convex, and the quasi-Newton algorithm is employed to solve the DFTSVM model. We also analyze the DFTSVM model to demonstrate its noise insensitivity and weighted scatter minimization. The fuzzy decision rule we define is independent of Gaussian assumptions, and a kernel version of DFTSVM via a reduced-set strategy is achieved. In the experimental part, we demonstrate the effectiveness of DFTSVM when handling uncertain objects. Moreover, we illustrate how the DFTSVM model is modified to deal with large-scale datasets by constructing probability density functions of samples and how to model deep features as distribution inputs for image datasets.

Deep graph clustering (DGC) has been a promising method for clustering graph data in recent years. However, existing research primarily focuses on optimizing clustering outcomes by improving the quality of embedded representations, resulting in slow-speed complex models. Additionally, these methods do not consider changes in node similarity and corresponding adjustments in the original structure during the iterative optimization process after updating node embeddings, which easily falls into the representation collapse issue. We introduce an Efficient Graph Auto-Encoder (EGAE) and a dynamic graph weight updating strategy to address these issues, forming the basis for our proposed Fast DGC (FastDGC) network. Specifically, we significantly reduce feature dimensions using a linear transformation that preserves the original node similarity. We then employ a single-layer graph convolutional filtering approximation to replace multiple layers of graph convolutional neural network, reducing computational complexity and parameter count. During iteration, we calculate the similarity between nodes using the linearly transformed features and periodically update the original graph structure to reduce edges with low similarity, thereby enhancing the learning of discriminative and cohesive representations. Theoretical analysis confirms that EGAE has lower computational complexity. Extensive experiments on standard datasets demonstrate that our proposed method improves clustering performance and achieves a speedup of 2–3 orders of magnitude compared to state-of-the-art methods, showcasing outstanding performance. The code for our model is available at https://github.com/Marigoldwu/FastDGC . Furthermore, we have organized a portion of the DGC code into a unified framework, available at https://github.com/Marigoldwu/A-Unified-Framework-for-Deep-Attribute-Graph-Clustering .

Image clustering is a research hotspot in machine learning and computer vision. Existing graph-based semi-supervised deep clustering methods suffer from three problems: 1) because clustering uses only high-level features, the detailed information contained in shallow-level features is ignored; 2) most feature extraction networks employ the step odd convolutional kernel, which results in an uneven distribution of receptive field intensity; and 3) because the adjacency matrix is precomputed and fixed, it cannot adapt to changes in the relationship between samples. To solve the above problems, we propose a novel graph-based semi-supervised deep clustering method for image clustering. First, the parity cross-convolutional feature extraction and fusion module is used to extract high-quality image features. Then, the clustering constraint layer is designed to improve the clustering efficiency. And, the output layer is customized to achieve unsupervised regularization training. Finally, the adjacency matrix is inferred by actual network prediction. A graph-based regularization method is adopted for unsupervised training networks. Experimental results show that our method significantly outperforms state-of-the-art methods on USPS, MNIST, street view house numbers (SVHN), and fashion MNIST (FMNIST) datasets in terms of ACC, normalized mutual information (NMI), and ARI.

Image denoising methods based on convolutional neural networks have been popular and achieved relatively excellent performance. However, most of the existing methods cannot fully obtain and use the shallow feature information when removing noise, and cannot better combine information between various network layers. In this paper, we propose an image denoising algorithm based on a feature enhancement network and multi-scale convGRU, named a shallow feature enhancement and multi-scale convGRU denoising network (SFEMGN), through an in-depth study of convolutional networks and GRU networks. We first propose a feature enhancement block to extract richer shallow features and enhance the protection of image details. Furthermore, the proposed SFEMGN integrates a multi-scale convolution GRU module, which can combine spatial features and temporal features at the same time. Comparative experiments and ablation studies demonstrate that our proposed model can achieve competitive performance in both gray and color image denoising tasks.

Communication learning is an effective way to solve complicated cooperative tasks in multi-agent reinforcement learning (MARL) domain.Graph neural network (GNN) has been widely adopt for learning the multi-agent communication and various GNN-based MARL methods have emerged.However, most of these methods are not specially designed for heterogeneous multi-agent scenarios, where agents have heterogeneous attributes or features based on different observation spaces or action sets.Without effective processing and transmission of heterogeneous feature information, communication learning will be useless and even reduce the performance of cooperation.To solve this problem, we propose a communication learning mechanism based on heterogeneous GNN and graph information maximization to learn effective communication for heterogeneous agents.Specifically, we use heterogeneous GNN for learning the efficient message representations, which aggregate the local feature information of neighboring agents.Furthermore, we maximize the mutual information (MI) between message representations and local values to make efficient use of information.Besides, we present a MARL framework that can flexibly integrate the proposed communication mechanism with existing value factorization methods.Experiments on various heterogeneous multi-agent scenarios demonstrate the effectiveness and superiority of the proposed method compared with baselines.

Most recent research on multiagent reinforcement learning (MARL) has explored how to deploy cooperative policies for homogeneous agents. However, realistic multiagent environments may contain heterogeneous agents that have different attributes or tasks. The heterogeneity of the agents and the diversity of relationships cause the learning of policy excessively tough. To tackle this difficulty, we present a novel method that employs a heterogeneous graph attention network to model the relationships between heterogeneous agents. The proposed method can generate an integrated feature representation for each agent by hierarchically aggregating latent feature information of neighbor agents, with the importance of the agent level and the relationship level being entirely considered. The method is agnostic to specific MARL methods and can be flexibly integrated with diverse value decomposition methods. We conduct experiments in predator-prey and StarCraft Multiagent Challenge (SMAC) environments, and the empirical results demonstrate that the performance of our method is superior to existing methods in several heterogeneous scenarios.

Fingerprint classification is one of the core steps of fingerprint recognition and directly relates to the accuracy of recognition. For this reason, a fingerprint classification method based on Twin Support Vector Machine (TWSVM) is studied. First, the Gabor filter is used to extract texture features from fingerprint images. Second, a multi‐class model based on TWSVM is constructed by using the ‘one‐versus‐all’ strategy and the binary tree method, respectively. The quantum particle swarm optimisation algorithm is used to optimise the parameters in the model. Then the fingerprints are divided into five categories using the optimised model. Finally, the classification model is evaluated using fingerprint images from the NIST‐4 database. The experimental results show that applying the TWSVM to fingerprint classification can get good classification results.

Image segmentation is a key step in the process of image data processing. The quality of image segmentation will directly affect the accuracy of image cognitive understanding. The purpose of image segmentation is to divide the image into regions with specific semantics. For the simple linear iterative clustering (SLIC) algorithm, the feature equalisation parameters need to be set manually during image segmentation, which results in the lack of segmentation effects and slow processing time. By introducing the theory of intermediary mathematics, an improved adaptive SLIC super‐pixel algorithm is proposed, which can adaptive generate characteristic equalisation parameters according to the specific situation of the image, thereby simplifying the operation steps and improving the image segmentation effect. After experimental verification and analysis, compared with the original SLIC algorithm and several other super‐pixel contrast algorithms, the algorithm in this study can effectively shorten the processing time and achieve a better segmentation effect.

Local feature extraction is one of the key characteristics of convolutional neural networks (CNNs). This study proposes an adaptive local feature enhancement (ALFE) model with a low‐frequency general appearance‐enhancement operator and a high‐frequency local detail enhancement operator to improve local features of CNNs. Through supervised training, the model could adaptively adjust enhancement parameters and achieve a global‐local enhancement of training images and CNNs. The performance of ALFE was first preliminarily evaluated with a self‐built CNN on CIFAR‐10 data set in different conditions of image augmentation and feature pooling. CNNs with ALFE could increase the top‐1 accuracy compared against CNNs in the same conditions, and nearly reach the same level of performance for different pooling approaches. With only two extra adjustable parameters, this model could effectively avoid overfitting, without affecting the convergence speed of CNN. In addition, the extra burden of network complexity could be neglected. Further, experiments of three existing popular CNNs (AlexNet, VGGNet and ResNet) with ALFE were carried out on dogs versus cats, Tiny ImageNet, and SVHN data sets, respectively. The results show that ALFE is feasible for the existing popular CNN models, improving their top‐1 accuracies without changing their convergence speeds.

Pulse coupled neural network (PCNN) is widely used in image processing because of its unique biological characteristics, which is suitable for image fusion. When combining PCNN with non‐subsampled contourlet (NSCT) model, it is applied in overcoming the difficulty of coefficients selection for subband of the NSCT model. However in the original model, only the grey values of image pixels are used as input, without considering that the subjective vision of human eyes lacks the sensitivity to the local factors of the image. In this study, the improved pulse‐coupled neural network model has replaced the grey‐scale value of the image and introduced the weighted product of the strength of the gradient of the image and the local phase coherence as the model input. Finally, compared with other multi‐scale decompositions‐based image fusion and other improved NSCT‐PCNN algorithms, the algorithm presented in this study outperforms them in terms of objective criteria and visual appearance.

Fingerprint enhancement plays a very important role in automatic fingerprint identification system. In order to ensure reliable fingerprint identification and improve fingerprint ridge structure, a novel method based on the collaborative filtering model for fingerprint enhancement is proposed. The proposed method consists of two stages. First, the original fingerprint is pre‐enhanced by using Gabor filter and linear contrast stretching. Next, the pre‐enhanced fingerprint is partitioned into patches in spatial domain, and then the patches are enhanced based on spectra diffusion by using the two‐dimensional (2D) angular‐pass filter and the 2D Butterworth band‐pass filter. The proposed method takes full advantage of the ridge information and spectra diffusion with higher quality to recover the lost ridge information. To evaluate proposed method, the databases FVC2004 are employed, and the comparison experiments are carried out using various methods. Comparative experimental results show that the proposed algorithm outperforms the existing state‐of‐the‐art methods on fingerprint enhancement.

The main task of image segmentation is to partition an image into disjoint sets of pixels called clusters. Spectral clustering algorithm has been developed rapidly in recent years and it has been widely used in image segmentation. The traditional spectral clustering algorithm requires huge amount of computation to process colour images with high resolution. While one possible solution is reducing image resolution, but it will lead to the loss of image information and reduce segmentation performance. To overcome the problem of traditional spectral clustering, an image segmentation algorithm based on superpixel clustering is proposed. Firstly, the algorithm uses the superpixel preprocessing technique to quickly divide the image into a certain number of superpixel regions with specific information. Then, the similarity matrix is used to provide the input information to the spectral clustering algorithm to cluster the superpixel regions and get the final image segmentation results. The experiment results show that the proposed algorithm can effectively improve the performance in image segmentation compared with the traditional spectral clustering algorithm, and finally the substantial improvement has been obtained in respect of computational complexity, processing time and the overall segmentation effect.

This research aims at finding a way to deal with the uncertain problem which has both positive evidence and negative evidence at the same time. Based on the linguistic truth-valued lattice implication algebra, we establish six-element linguistic truth-valued linguistic intuitionistic fuzzy first-order logic system. Subsequently, we present a resolution method of six-element linguistic truth-valued linguistic intuitionistic fuzzy first-order logic (6LTV_IFFL) and prove the soundness of it. Meanwhile, the step of resolution method of 6LTV_IFFL is given.

Recently proposed multiple birth support vector machine (MBSVM), which is extended from TWSVM, is an efficient algorithm for multi-class classification. MBSVM keeps the advantage of TWSVM. However, the solution of MBSVM classifier follows solving quadratic programming. Solving quadratic programming requires long time. This paper presents a granular multiple birth support vector machine based on weighted linear loss (WLGMBSVM) to enhance the performance of MBSVM classifier. WLGMBSVM uses the strategy of “all-versus-one” as MBSVM does. By introducing the weighted linear loss, the proposed algorithm only needs to solve simple linear equations. Inspired by granular support vector machine, WLGMBSVM handles classification in granules. The experiments also show the efficiency of WLGMBSVM.

The training algorithm of classical twin support vector regression (TSVR) can be attributed to the solution of a pair of quadratic programming problems (QPPs) with inequality constraints in the dual space. However, this solution is affected by time and memory constraints when dealing with large datasets. In this paper, we present a least squares version for TSVR in the primal space, termed primal least squares TSVR (PLSTSVR). By introducing the least squares method, the inequality constraints of TSVR are transformed into equality constraints. Furthermore, we attempt to directly solve the two QPPs with equality constraints in the primal space instead of the dual space; thus, we need only to solve two systems of linear equations instead of two QPPs. Experimental results on artificial and benchmark datasets show that PLSTSVR has comparable accuracy to TSVR but with considerably less computational time. We further investigate its validity in predicting the opening price of stock.

A novel method based on rough sets (RS) and the affinity propagation (AP) clustering algorithm is developed to optimize a radial basis function neural network (RBFNN). First, attribute reduction (AR) based on RS theory, as a preprocessor of RBFNN, is presented to eliminate noise and redundant attributes of datasets while determining the number of neurons in the input layer of RBFNN. Second, an AP clustering algorithm is proposed to search for the centers and their widths without a priori knowledge about the number of clusters. These parameters are transferred to the RBF units of RBFNN as the centers and widths of the RBF function. Then the weights connecting the hidden layer and output layer are evaluated and adjusted using the least square method (LSM) according to the output of the RBF units and desired output. Experimental results show that the proposed method has a more powerful generalization capability than conventional methods for an RBFNN.

In this paper, we analyze the information feature of principal component analysis (PCA) deeply based on information entropy. According to idea of entropy function, a new weighted information functions (WIF) is proposed, and the information content of data matrix X is measured by it. Based on WIF, the information compression rate (ICR, RIC) and accumulated information compression rate (AICR, RIC) are set up, by which the degree of information compression is measured. At last, an improved PCA algorithm (IPCA) based on WIF is constructed. Through simulated application in practice, the results show that the IPCA proposed here is efficient and satisfactory. It provides a new research approach of feature compression for pattern recognition, machine learning, data mining and so on.