Multimodal Emotion Recognition (MER) aims to understand complex human emotions by jointly analyzing visual and textual data. However, in real-world scenarios, emotional cues from different modalities often contain conflict information, such as a smiling face paired with negative text, which poses great challenges for existing multimodal language models (MLLMs). Existing emotion MLLMs and multimodal emotion benchmarks often overlook or even intentionally avoid scenarios involving multimodal emotion conflicts, limiting their ability to reason about complex and contradictory affective cues. By addressing this, we propose Conflict-Explicit Reflective Agent (CERA), a training-free, conflict-aware, and language-driven agentic framework for MER. The concept of CERA is to treat modality emotion conflicts as meaningful signals and resolve them via a three-stage perception–evaluation–reflection reasoning loop. Firstly, the agent’s conflict-perceptive emotion graph construction module builds emotion graphs from fine-grained cues to reveal conflicts, and progressively refines them through iterative updates. Secondly, a reward model evaluates these graphs and produces natural language feedback that identifies unresolved conflicts. Lastly, the language-driven conflict refinement module generates graph editing signals from the feedback without any parameter tuning, enabling the overall CERA to refine its reasoning without training. Extensive experiments on two multimodal emotion datasets, MAFW and CH-SIMS, demonstrate that CERA significantly outperforms state-of-the-art training-free methods in both recognition accuracy and conflict interpretability, providing an effective training-free solution for complex emotional reasoning.

Multimodal clustering (MMC) overcomes the limitations of unimodal methods by integrating information from multiple sources, but the complexity of heterogeneous information coupling hinders effective feature extraction. Critically, existing MMC paradigms primarily focus on capturing consensus through coarse-grained cross-modal alignment. However, such task-agnostic strategies overlook the differences in the utility of feature information across varying task environments. In the absence of task-centric guidance, models often struggle to effectively distinguish task-relevant critical information from task-irrelevant redundant noise during the disentanglement process, leading to information confusion in the representation space. To address this challenge, we propose a deep disentangled multimodal clustering method guided by information theory, named DRLMMC, which employs a tripartite information optimization mechanism to achieve deep disentanglement of cross-modal representations. 1) We design modality-specific encoders to construct nonlinear mapping spaces, transforming the reconstruction mechanism of autoencoders into an information-theoretic mutual information (MI) constraint problem, preserving the unique features of different modalities; 2) To establish cross-modal semantic associations, it constructs a cross-modal shared information extraction module, and, based on an information-theoretic framework, designs an optimization objective function to progressively align multimodal feature subspaces through MI maximization and contrastive learning, capturing task-relevant invariant features across modalities; 3) A unique information dynamic perception module is proposed, which employs a conditional MI projection network combined with learning distribution regularization to adaptively extract and enhance modality-specific task-relevant unique information. Experimental results demonstrate that DRLMMC outperforms existing state-of-the-art methods on multimodal benchmark datasets, exhibiting excellent generalization ability. Notably, it achieves precise disentanglement of cross-omics features in multi-omics analysis, offering a novel methodological approach for handling complex biomedical data.

Anchor graph learning has become a widely used technique for significantly reducing the computational complexity in existing multi-view clustering methods. However, most existing approaches select anchors independently for each view and then generate the consensus graph by directly fusing all anchor graphs. This process overlooks the correspondence between anchor sets across different views, i.e., the column order correspondence of the anchor graphs. To address this limitation, we propose a novel anchor-based tensor multi-rank constraint multi-view clustering method (TMC). Specifically, TMC captures the high-order structural information of the original data by constructing an anchor graph tensor and enforcing a multi-rank constraint to induce a block-diagonal structure. Additionally, to enhance anchor consistency across all view, we construct the anchor graph of each view into an anchor tensor and impose a low-rank constraint on it. In this way, the block-diagonal structure of each anchor graph maintains an approximate alignment between anchors. Furthermore, we provide theoretical proof that the generated anchor graphs inherently exhibit a block-diagonal structure. Extensive experimental results on six multi-view datasets demonstrate that TMC outperforms existing state-of-the-art methods, highlighting its effectiveness in multi-view clustering task.

Multi-View Clustering (MVC) has gained increasing attention due to its ability to effectively leverage the complementary information of multi-view data. Despite the success of existing MVC methods in many real-world applications, they often overlook the discrepancy of view-specific latent distribution and struggle to ensure the completeness of the multi-view data. To address these challenges and harness the powerful feature extraction capability of deep networks, we propose a novel Contrastive and Dual Adversarial Representation Learning method for Multi-view Clustering, termed as CDARL, to solve multi-view clustering problems with both complete and incomplete multi-view data. Specifically, CDARL employs alternating adversarial and contrastive learning to align the view-specific representations, driving them into the same semantic latent space to minimize the discrepancy in view-specific distributions. In addition, a consensus latent representation is learned by an adaptive fusion block that integrates information from multiple views. The consensus representation is further refined through adversarial learning modeling the transformation of the standard Gaussian distribution to the original data distribution. Moreover, the proposed method incorporates an imputation strategy designed to handle the incomplete multi-view data clustering task. This strategy utilizes both reconstructed samples and cross-view neighbors to impute missing views from the latent space and the original space, thereby preserving clustering information, which ensures the quality and feasibility of the imputed samples. Experimental results on six widely used datasets have verified the competitiveness of the proposed CDARL method against state-of-the-art methods in MVC problems with complete and incomplete multi-view data. Code is available athttps://github.com/xywy220/CDARL-MVC.

Incomplete multi-view clustering (IMVC) has attracted increasing attention in recent years, owing to the prevalence of missing data in real-world multi-view scenarios. Existing imputation-based IMVC methods partially mitigate the impact of missing information but still face three key limitations: (i) overlooking latent structural relationships among samples, which leads to imputed representations deviating from the true distribution; (ii) decoupling imputation from clustering, which reduces the discriminability of the recovered representations; and (iii) exhibiting low efficiency, which makes it difficult to balance recovery quality and inference speed under complex missing scenarios. To address these issues, we propose a Structure-Aware Conditional Diffusion Generation (SACDG) framework. During training, SACDG first models local structural relationships via adaptive neighborhood graphs and injects them as conditional priors into the diffusion model, where a cross-attention mechanism integrates these priors into the noise prediction process to learn structure-aware generative capability. Meanwhile, a semantic distribution alignment module is introduced to leverage pseudo-labels for enforcing cross-view consistency, thereby enhancing semantic discriminability. During inference, SACDG integrates cross-view structural information through cross-view adjacency fusion to guide the reverse denoising trajectory, and employs deterministic DDIM sampling to efficiently and stably recover the representations of missing views. Extensive comparative experiments and ablation studies on multiple benchmark datasets demonstrate that SACDG achieves superior clustering performance and improved efficiency over state-of-the-art methods. Our code is available athttps://github.com/zhangyuanyang21/SACDG.

Spatially resolved transcriptomics (SRT) facilitates the simultaneous acquisition of gene expression profiles, spatial location, and histology images for spatial clustering analysis, providing transformative insights into cellular interactions and the underlying mechanisms of disease progression. Despite the success of existing research in spatial clustering tasks, most methods overlook the information imbalance arising among spots in intra- and inter-modal communication due to insufficient sequencing depth and modality discrepancies. To this end, we propose a novel multi-view information rebalance learning method for SRT data clustering, referred to as MIRL. Specifically, we construct hypergraphs for the gene and histological image modalities and leverage hypergraph neural networks to learn the hypergraph features, which helps mitigate the propagation of intra-modal information imbalance by capturing higher-order interactions among multiple spots, rather than relying solely on pairwise relationships in traditional feature graphs. To enhance the global coordination among spots and the interrelations between features across modalities, we perform intra-modal adaptive fusion of modality-specific hypergraph features and spatial features, followed by cross-modal integration. Furthermore, adaptive reconstruction of the cross-modal heterogeneous graph is employed to rebalance inter-modal information flow associated with pseudo-labels, ensuring more reliable information extraction by alleviating the impact of incorrect heterogeneous negative edges connections through the construction of hypergraph edges. Extensive experimental results demonstrate that the proposed MIRL achieves competitive performance in spatial domain identification compared to other state-of-the-art ones.

Multi-view clustering (MVC) for remote sensing data has demonstrated significant potential in Earth observation, given its ability to aggregate multi-source information without relying on labels. Despite achieving compelling results through the combination of deep encoders and contrastive learning, existing algorithms still face two limitations: inadequate exploration of diverse spatial relationships and inability to guide the selection of sample pairs leads to blind sampling, both of which lead to suboptimal clustering performance. To tackle these challenges, we propose a sampling enhanced contrastive multi-view clustering method for remote sensing data, namely SEC-LSRM. The proposed method incorporates long- and short-range information mining to enhance clustering performance. By aggregating shortrange information extracted through autoencoders and longrange information obtained via graph autoencoders, our method improves the sampling quality of positive and negative sample pairs. To render the extracted features more compact, a multiview correlation reduction strategy is devised to filter out irrelevant information. With the extracted comprehensive features, an adaptive sampling strategy is designed to obtain high-quality positive and negative samples. Subsequently, we select positive and negative sample pairs based on these affinity matrices with idempotence and block diagonal constraints. Moreover, we integrate the optimization of these sample pairs and contrastive learning within the same framework to achieve iterative updates of both. Experiments conducted on multiple multi-view remote sensing datasets illustrate that our proposed SEC-LSRM method achieves excellent and reliable clustering performance.

Graph-based multi-view clustering methods have demonstrated satisfying performance by effectively capturing relationships among data samples. However, most existing methods primarily emphasize direct pairwise relationships, neglecting the exploration of high-order correlations present within each view. To this end, a novel approach, called multiview clustering via high-order bipartite graph learning and tensor low-rank representation (HBGTLRR), is proposed. Specifically, we first construct high-order bipartite graphs to capture latent relationships and concatenate them into a tensor. By applying tensor nuclear norm (TNN) minimization, we obtain a low-rank representation that reduces noise and preserves high-order consistency. Subsequently, a consensus graph is constructed by adaptively fusing the high-order bipartite graphs with corresponding weights, and then a Laplacian low-rank constraint is imposed on it to effectively capture the intrinsic data structure. Finally, extensive experimental results show that HBGTLRR significantly outperforms existing methods, thereby validating the effectiveness of our proposed method.

Multiple kernel clustering (MKC) enhances clustering performance by deriving a consensus partition or graph from a predefined set of kernels. Despite many advanced MKC methods proposed in recent years, the prevalent approaches involve incorporating all kernels by default to capture diverse information within the data. However, learning from all kernels may not be better than one of a few kernels, particularly since some kernels exhibit a higher proportion of noise than semantic content. Additionally, existing MKC methods, whether based on early-fusion or late-fusion approaches, predominantly rely on pairwise relationships among samples or cluster structures, neglecting potential correlations between these two aspects. To this end, we propose a multiple kernel clustering with an adaptive multi-scale partition selection method (MPS), which exploits multiple-dimensional representations and the pairwise cluster structure for clustering. By the proposed kernel selection framework, potentially harmful kernels are dynamically excluded during the kernel fusion process, and then the multi-scale partitions and similarity graphs derived from the retained kernels are utilized to facilitate the improved consensus partition generation. Finally, extensive experiments are conducted to demonstrate the effectiveness of MPS on eight benchmark datasets.

Multiple Kernel Clustering (MKC) has emerged as a prominent research domain in recent decades due to its capacity to exploit diverse information from multiple views by learning an optimal kernel. Despite the successes achieved by various MKC methods, a significant challenge lies in the computational complexity associated with generating a consensus partition from the optimal kernel matrix, typically of size$n \times n$, where$n$represents the number of samples. This computational bottleneck restricts the practical applicability of these methods when confronted with large-scale datasets. Furthermore, certain existing MKC algorithms derive the consensus partition matrix by fusing all base partitions. However, this fusion process may inadvertently overlook critical information embedded in individual base kernels, potentially leading to inferior clustering performance. In light of these challenges, we introduce an innovative and efficient multiple kernel$k$-means approach, denoted as FAMKKM. Notably, FAMKKM incorporates two approximated partition matrices instead of the original individual partition matric for each base kernel. This strategic substitution significantly reduces computational complexity. Additionally, FAMKKM leverages the original kernel information to guide the fusion of all base partitions, thereby enhancing the quality of the resulting consensus partition matrix. Finally, we substantiate the efficacy and efficiency of the proposed FAMKKM through extensive experiments conducted on six benchmark datasets. Our results demonstrate its superiority over state-of-the-art methods. The demo code of this work is publicly available athttps://github.com/WangJun2023/FAMKKM

Multiple kernel clustering (MKC) aims to extract and integrate the clustering information from a set of pre-defined kernels for handling data which cannot be linearly separated well. More precisely, existing MKC methods generally devote to learn the complementary information from a set of kernel partitions, whose feature dimensions are commonly fixed as the upper bound$n$or lower bound$c$, where$n$and$c$represents the number of samples and clusters, respectively. However, the adopting of the lower bound or upper bound generally leads to poor clustering performance caused by the lack or redundancy of clustering information carried by kernel partitions. To tackle this issue, we propose a novel late fusion multiple kernel clustering method, termed as Eigenvalue Ratio Inspired Partition Learning and Fusion for Multiple Kernel Clustering (ERMKC), in this paper. Specifically, we propose an eigenvalue ratio based criterion to guide the kernel partition learning for each single kernel matrix, which ensures more suitable feature dimensions for the learnt kernel partitions. In addition, we also propose a novel late fusion model for fusing the learnt kernel partitions optimally. Furthermore, we conduct extensive experiments on numerous benchmark datasets to evaluate the proposed ERMKC method, whose results verify the effectiveness and advantage of the proposed method compared to the other state-of-the-art methods.

Spatial transcriptomics technology fully leverages spatial location and gene expression information for spatial clustering tasks. However, existing spatial clustering methods primarily concentrate on utilizing the complementary features between spatial and gene expression information, while overlooking the discriminative features during the integration process. Consequently, the discriminative capability of node representation in the gene expression features is limited. Besides, most existing methods lack a flexible combination mechanism to adaptively integrate spatial and gene expression information. To this end, we propose an end-to-end deep learning method named MAFN for spatially resolved transcriptomics data clustering via a multi-view adaptive fusion network. Specifically, we first adaptively learn inter-view complementary features from spatial and gene expression information. To improve the discriminative capability of gene expression nodes by utilizing spatial information, we employ two GCN encoders to learn intra-view specific features and design a Cross-view Correlation Reduction (CCR) strategy to filter the irrelevant information. Moreover, considering the distinct characteristics of each view, a Cross-view Attention Module (CAM) is utilized to adaptively fuse the multi-view features. Extensive experimental results demonstrate that the proposed MAFN achieves competitive performance in spatial domain identification compared to other state-of-the-art ones.

Multi-view spectral clustering, which exploits the complementary information among graphs of diverse views to obtain superior clustering results, has attracted intensive attention recently. However, most existing multi-view spectral clustering methods obtain the clustering partitions in a two-step scheme, i.e., spectral embedding and subsequent$k$-means. This two-step scheme inevitably seeks sub-optimal clustering results due to the information loss during the two-steps processes. Besides, existing multi-view spectral clustering methods do not jointly utilize the information of graphs and embedding matrices, which also degrades final clustering results. To solve these issues, we propose a unified one-step multi-view spectral clustering method, which integrates the spectral embedding and$k$-means into a unified framework to obtain discrete clustering labels with a one-step strategy. Under the observation that the inner product of the embedding matrix is a low-rank approximation of the graph, we combine graphs and embedding matrices of different views to obtain a unified graph. Then, we directly capture the discrete clustering indicator matrix from the unified graph. Furthermore, we design an effective optimization algorithm to solve the resultant problem. Finally, a set of experiments on various datasets are conducted to verify the effectiveness of the proposed method. The demo code of this work is publicly available atrgb]0,0,1https://github.com/guanyuezhen/UOMvSC.

Although demonstrating great success, previous multi-view unsupervised feature selection (MV-UFS) methods often construct a view-specific similarity graph and characterize the local structure of data within each single view. In such a way, the cross-view information could be ignored. In addition, they usually assume that different feature views are projected from a latent feature space while the diversity of different views cannot be fully captured. In this work, we resent a MV-UFS model via cross-view local structure preserved diversity and consensus learning, referred to as CvLP-DCL briefly. In order to exploit both the shared and distinguishing information across different views, we project each view into a label space, which consists of a consensus part and a view-specific part. Therefore, we regularize the fact that different views represent same samples. Meanwhile, a cross-view similarity graph learning term with matrix-induced regularization is embedded to preserve the local structure of data in the label space. By imposing the$l_{2,1}$-norm on the feature projection matrices for constraining row sparsity, discriminative features can be selected from different views. An efficient algorithm is designed to solve the resultant optimization problem and extensive experiments on six publicly datasets are conducted to validate the effectiveness of the proposed CvLP-DCL.

Simultaneous clustering and optimization (SCO) has recently drawn much attention due to its wide range of practical applications. Many methods have been previously proposed to solve this problem and obtain the optimal model. However, when a dataset evolves over time, those existing methods have to update the model frequently to guarantee accuracy; such updating is computationally infeasible. In this paper, we propose a new formulation of SCO to handle evolving datasets. Specifically, we propose a new variant of the alternating direction method of multipliers (ADMM) to solve this problem efficiently. The guarantee of model accuracy is analyzed theoretically for two specific tasks: ridge regression and convex clustering. Extensive empirical studies confirm the effectiveness of our method.

Feature extraction and feature selection have been regarded as two independent dimensionality reduction methods in most of the existing literature. In this paper, we propose to integrate both approaches into a unified framework and design an unsupervised linear feature selective projection (FSP) for feature extraction with low-rank embedding and dual Laplacian regularization, with the aim to exploit the intrinsic relationship among data and suppress the impact of noise. Specifically, a projection matrix with an l2,1-norm regularization is introduced to project original high dimensional data points into a new subspace with lower dimension, where the l2,1-norm regularization can endow the projection with good interpretability. We deploy a coefficient matrix with low rank constraint to reconstruct the data points and the l2,1-norm is imposed to regularize the data reconstruction errors in the low-dimensional subspace and make FSP robust to noise. Furthermore, a dual graph Laplacian regularization term is imposed on the low dimensional data and data reconstruction matrix for preserving the local manifold geometrical structure of data. Finally, an alternatively iterative algorithm is carefully designed for solving the proposed optimization model. Theoretical convergence and computational complexity analysis of the algorithm are also provided. Comprehensive experiments on various benchmark datasets have been carried out to evaluate the performance of the proposed FSP. As indicated, our algorithm significantly outperforms other state-of-the-art methods for feature extraction.