Accurate localization of oriented objects in remote sensing images (RSI) faces complex challenges, particularly the difficulty of feature modeling arising from the geometric complexity of oriented targets, manifested as arbitrary orientations and scale variations. Existing paradigms, primarily based on Convolutional Neural Networks (CNNs) or Transformers, which struggle to capture robust rotation-invariant features due to inherent structural deficiencies (feature confusion due to fixed grid sampling in CNNs, spatial misalignment due to inadequate orientation encoding in attention mechanism). Graph-based feature modeling displays robustness against geometric complexity, prompting several studies to explore the incorporation of graph structures. However, these methods primarily focus on region proposals or pixel-level rotation correlations, struggling to effectively model the rotation-invariant features of targets. In this paper, we propose the Graph-based Semantic Rerouting Interaction (GSRI), shifting the modeling of rotated semantic representation for objects from tilted spatial-domain pixel recognition to the exploration of consistent channel-level semantic responses, thereby achieving robust rotation semantic feature modeling. Specifically, GSRI introduces a dynamic dilated KNN based graph structure to enhance the rotation-invariant feature modeling through capturing the consistent activation relationships of similar targets in high-dimensional feature space. By incorporating a cross-attention mechanism with dual positional encoding supervision, GSRI facilitates the alignment and propagation of robust rotational semantics across cross-scale feature maps, thereby mitigating background noise and bolstering the perceptibility of rotated objects in complex RSI scenarios. Extensive experiments on the DOTA-v1.0 and DIOR-R datasets demonstrate the superior effectiveness of our method.

Detecting fake news in short videos is crucial for combating misinformation. Existing methods utilize topic modeling and co-attention mechanism, overlooking the modality heterogeneity and resulting in suboptimal performance. To address this issue, we introduce Text-Guided Fine-grained Counterfactual Inference for Short Video Fake News detection (TGFC-SVFN). TGFC-SVFN leverages modality bias removal and teacher-model-enhanced inter-modal knowledge distillation to integrate the heterogeneous modalities in short videos. Specifically, we use causality-based reasoning prompts guided text as teacher model, which then transfers knowledge to the video and audio student models. Subsequently, a multi-head attention mechanism is employed to fuse information from different modalities. In each module, we utilize fine-grained counterfactual inference based on a diffusion model to eliminate modality bias. Experimental results on publicly available fake short video news datasets demonstrate that our method outperforms state-of-the-art techniques.

Relation reasoning in temporal knowledge graph infers unknown or emerging relational dependencies from historical structured data. Traditional approaches face inherent limitations in capturing complex semantic correlations and structural patterns among relations. To tackle this problem, we propose the Relational Multi-path Enhancement network (RME), which primarily focuses on relation modeling to enrich relation representations through comprehensive multi-path analysis. RME consists of five key components: (1) Controlled random walk module creates multi-hop head-to-tail paths using an adaptive stopping rule that balances short- and long-term connections. (2) Shared path extraction module identifies both shared-head paths and shared-tail paths. (3) Time-decayed path encoding module processes these paths differently. (4) Gated information aggregation module combines path information to determine which parts matter most. (5) Attention decoding module makes the final prediction by focusing on the most relevant path features. Experiments on multiple TKG benchmark datasets demonstrate that RME outperforms the state-of-the-art methods in relation multi-path reasoning.

Current large vision-language models (VLMs) exhibit remarkable performance in basic video understanding tasks. However, existing VLMs are still limited to surface-level perception and lack fine-grained spatio-temporal understanding and combinatorial reasoning capabilities. Existing methods typically rely on expensive human annotations or subtitle extraction, yet they struggle to effectively model temporal relations between frames. This paper proposes a structured representation based on temporal triplets to address two major challenges in traditional approaches: temporal fragmentation and entity reference ambiguity. By modeling objects, attributes, and relationships within the video and incorporating temporal information, we convert semantic content from keyframes into a sequence of temporal triplets. This structured representation is then used as input for zero-shot video question answering (VideoQA). Experiments were conducted on four benchmark VideoQA datasets: NExT-QA, STAR, MSVD-QA, and MSRVTT-QA, showing that our method achieves competitive performance without requiring fine-tuning, validating its generality and effectiveness.

Temporal knowledge graph (TKG) reasoning has become a hot topic due to its great value in many practical tasks. The key to TKG reasoning is modeling the structural information and evolutional patterns of the TKGs. While great efforts have been devoted to TKG reasoning, the structural and evolutional characteristics of real-world networks have not been considered. In the aspect of structure, real-world networks usually exhibit clear community structure and scale-free (long-tailed distribution) properties. In the aspect of evolution, the impact of an event decays with the time elapsing. In this paper, we propose a novel TKG reasoning model called Hawkes process-based Evolutional Representation Learning Network (HERLN), which learns structural information and evolutional patterns of a TKG simultaneously, considering the characteristics of real-world networks: community structure, scale-free and temporal decaying. First, we find communities in the input TKG to make the encoding get more similar intra-community embeddings. Second, we design a Hawkes process-based relational graph convolutional network to cope with the event impact-decaying phenomenon. Third, we design a conditional decoding method to alleviate biases towards frequent entities caused by long-tailed distribution. Experimental results show that HERLN achieves significant improvements over the state-of-the-art models.

Conditional semantic textual similarity (C-STS) assesses the similarity between pairs of sentence representations under different conditions. The current method encounters the over-estimation issue of positive and negative samples. Specifically, the similarity within positive samples is excessively high, while that within negative samples is excessively low. In this paper, we focus on the C-STS task and develop a conditional contrastive learning framework that constructs positive and negative samples from two perspectives, achieving the following primary objectives: (1) adaptive selection of the optimization direction for positive and negative samples to solve the over-estimation problem, (2) fully balance of the effects of hard and false negative samples. We validate the proposed method with five models based on bi-encoder and tri-encoder architectures, the results show that our proposed method achieves state-of-the-art performance. The code is available at https://github.com/qinzeyang0919/CCL.

Online continual learning (OCL) is a strict setting of continual learning (CL), where the OCL agent faces a never-ending data stream and encounters each new sample only once. An OCL agent suffers more serious catastrophic forgetting (i.e., forgetting previous knowledge of old classes) than a CL agent. Existing OCL methods leverage the contrastive-based losses to improve the classifier’s ability against forgetting. However, almost all these methods ignore the role of hard negative samples in these losses, and these samples are difficult to distinguish from positive samples, exacerbating catastrophic forgetting. In this paper, we focus on the classification task in the online contrastive continual learning (OCCL) setting and propose a novel contrastive-based OCL method named OCCL with Hard Negative Samples (OHNS) which emphasizes the importance of hard negative samples. Concretely, OHNS designs an adaptive weight to measure the hardness of the negative sample and proposes a new contrastive-based loss function by combining this loss function with the weight to enhance the classification strength on hard negative samples. We conduct extensive experiments on three real-world benchmark datasets, and the results demonstrate the superiority of OHNS over various state-of-the-art OCL methods.

Linear attention reduces the quadratic computational complexity of the Transformer’s Softmax attention to linearity. However, it restricts the emphasis on critical regions and diminishes feature diversity. To address these limitations, we propose the Selective Sampling Softmax and Multi-Feature Enhancement Linear (S3ML) Attention mechanism. First, we design a selective sampling Softmax attention to efficiently emphasize essential regions with constrained computational costs. Second, we propose a feature enhancing module, which integrates external channel attention, focused linear attention and token interaction to enrich feature diversity. Leveraging the S3ML attention mechanism, we construct a series of Vision Transformer models named S3ML-ViT. Classification experiments on ImageNet-1K and object detection tests on COCO2017 show that S3ML-ViT balances performance and efficiency effectively, highlighting its strong potential for downstream tasks. Our code is available at https://github.com/Senqi-Guan/Linear_Attention/tree/main/S3ML-ViT.

Short video platforms are popular for sharing information, but they also spread harmful content quickly. Research on detecting harmful videos is limited due to unclear categories and a lack of good datasets. To solve this, we created CH-SV, the first Chinese Harmful Short Video dataset with 6,728 videos labeled into six categories: danger, offense, vulgarity, fakeness, violence, and normal content. We analyzed CH-SV in detail and proposed HAVE, a new detection framework that improves video understanding using AI-generated semantics. Experiments on CH-SV show that our dataset can help advance harmful video detection research, and HAVE outperforms existing methods. Resources including core code, dataset samples, supplementary material, and licensing details are available at https://github.com/DLUTSSL/CH-SV.

In deep reinforcement learning (DRL), the presence of dormant neurons leads to a significant reduction in network capacity, which results in sub-optimal performance and limited sample efficiency. Existing training techniques, especially those relying on periodic resetting (PR), exacerbate this issue. We propose the Full Network Capacity (FNC) framework based on PR, which consists of two novel modules: Dormant Neuron Reactivation (DNR) and Stable Policy Update (SPU). DNR continuously reactivates dormant neurons, thereby enhancing network capacity. SPU mitigates perturbation from DNR and PR and stabilizes the Q-values for the actor, ensuring smooth training and reliable policy updates. Our experimental evaluations on the Atari 100K and DMControl 100K benchmarks demonstrate the remarkable sample efficiency of FNC. On Atari 100K, FNC achieves a superhuman IQM HNS of 107.3%, outperforming the previous state-of-the-art method BBF by 13.3%. On DMControl 100K, FNC excels in 5 out of 6 tasks in terms of episodic return and attains the highest median and mean aggregated scores. FNC not only maximizes network capacity but also provides a practical solution for real-world applications where data collection is costly and time-consuming. Our implementation is publicly accessible at \url{https://github.com/tlyy/FNC}.

Multi-goal conversational recommender system (MG-CRS) which is more in line with realistic scenarios has attracted a lot of attention. MG-CRS can dynamically capture the demands of users in conversation, continuously engage their interests, and make recommendations. The key of accomplishing these tasks is to plan a reasonable goal sequence which can naturally guide the user to accept the recommended goal. Previous works have demonstrated that mining the correlations of goals from the goal sequences in the dialogue corpus is helpful for recommending the goal that the user is interested in. However, they independently model correlations for each level of goal (i.e., goal type or entity) and neglect the order of goals appear in the dialogue. In this paper, we propose a goal interaction graph planning framework which constructs a directed heterogeneous graph to flexibly model the correlations between any level of goals and retain the order of goals. We design a goal interaction graph learning module to model the goal correlations and propagate goal representations via directed edges, then use an encoder and a dual-way fusion decoder to extract the most relevant information with the current goal from the conversation and domain knowledge, making the next-goal prediction fully exploit the prior goal correlations and user feedback. Finally we generate engaging responses based on the predicted goal sequence to complete the recommendation task. Experiments on two benchmark datasets show that our method achieves significant improvements in both the goal planning and response generation tasks.

Video question answering involves understanding video content to generate accurate answers to questions. Recent studies have successfully modeled video features and achieved diverse multimodal interaction, yielding impressive outcomes. However, they have overlooked the fact that the video contains richer instances and events beyond the scope of the stated question. Extremely imbalanced alignment of information from both sides leads to significant instability in reasoning. To address this concern, we propose the Video-Context Aligned Transformer (V-CAT), which leverages the context to achieve semantic and content alignment between video and question. Specifically, the video and text are encoded into a shared semantic space initially. We apply contrastive learning to global video token and context token to enhance the semantic alignment. Then, the pooled context feature is utilized to obtain corresponding visual content. Finally, the answer is decoded by integrating the refined video and question features. We evaluate the effectiveness of V-CAT on MSVD-QA and MSRVTT-QA dataset, both achieving state-of-the-art performance. Extended experiments further analyze and demonstrate the effectiveness of each proposed module.

Generative conversational agents, driven by large language models, have gained widespread popularity. However, a significant drawback lies in their tendency to produce uncontrollable and unpredictable contents, thereby increasing the risk of generating malevolent responses that potentially exacerbate users’ mental health issues. Although existing research on malevolence detection in dialogues addressed the modeling of interaction patterns in dialogues, the implicitly expressed emotion and user-specific context are often neglected. Addressing this gap, we propose a hypergraph-enhanced context modeling approach for detecting malevolence in mental health counseling dialogues. Our approach harnesses the emotion reasoning capabilities of large language models to generate implicit emotional prompts. Employing hypergraph neural networks, our approach effectively integrates emotional context, user-specific context, and interactive context, fusing them into high-order semantic representations using hypergraph convolution. Experimental results on two benchmark datasets, MDRDC and Dialogue Safety, demonstrate the superiority of our model over state-of-the-art baseline models, particularly in complex contextual scenarios.

Reasoning over the Temporal Knowledge Graph (TKG) that predicts facts in the future has received much attention. Most previous works attempt to model temporal dynamics with knowledge graphs and graph convolution networks. However, these methods lack the consideration of high-order interactions between objects in TKG, which is an important factor to predict future facts. To address this problem, we introduce dynamic hypergraph embedding for temporal knowledge graph reasoning. Specifically, we obtain high-order interactions by constructing hypergraphs based on temporal knowledge graphs at different timestamps. Besides, we integrate the differences caused by time into the hypergraph representation in order to fit TKG. Then, we adapt dynamic meta-embedding for temporal hypergraph representation that allows our model to choose the appropriate high-order interactions for downstream reasoning. Experimental results on public TKG datasets show that our method outperforms the baselines. Furthermore, the analysis part demonstrates that the proposed method brings good interpretation for the predicted results.

Temporal knowledge graph completion is a critical task within the knowledge graph domain. Existing approaches encompass deep neural network-based methods for temporal knowledge graph embedding and rule-based logical symbolic reasoning. However, the former may not adequately account for structural dependencies between relations.Conversely, the latter methods relies heavily on strict logical rule reasoning and lacks robustness in the face of fuzzy or noisy data. In response to these challenges, we present RENN, a groundbreaking framework that enhances temporal knowledge graph completion through rule embedding. RENN employs a three-step approach. First, it utilizes temporary random walk to extract temporal logic rules. Then, it pre-trains by learning embeddings for each logical rule and its associated relations, thereby enhancing the likelihood of existing quadruples and logical rules. Finally, it incorporates the embeddings of logical rules into the deep neural network. Our methodology has been validated through experiments conducted on various temporal knowledge graph models and datasets, consistently demonstrating its effectiveness and potential in improving temporal knowledge graph completion.

A continual learning agent, which faces a never-ending stream of data, suffers from severe catastrophic forgetting. To prevent forgetting, memory-based methods have shown more effective performance by retaining fractional previous data in a fixed-size memory buffer to maintain the observed category information. Nevertheless, which samples should be kept in the memory buffer is still an open question, and existing methods rarely address this issue from the perspective of sample information. In this work, we contribute a concise yet effective memory update method, Maximum Entropy Memory Update (MEMU). MEMU retains the samples adjacent to the decision boundaries since we observe that these samples have higher entropy. To this end, we design an indicator to score each sample and retain higher-score samples in the buffer. Compared to the state-of-the-art benchmarks, the experiments demonstrate that MEMU improves performance on five data streams with three metrics in the online continual learning setting.

Open-ended VideoQA presents a significant challenge due to the absence of fixed options, requiring the identification of the correct answer from a vast pool of candidate answers. Previous approaches typically utilize classifier or similarity comparison on fusion feature to yield prediction directly, lacking coarse-to-fine filtering on numerous candidates. Gradual refining the probability distribution of candidates can achieve more precise prediction. Thus, we propose the DiffAns model, which integrates the diffusion model to handle open-ended VideoQA task, simulating the gradual process by which humans answer open-ended question. Specifically, we first diffuse the true answer label into a random distribution (forward process). And under the guidance of answer-aware condition generated from video and question, the model iteratively denoises to obtain the correct probability distribution (backward process). This equips the model with the capability to progressively refine the random probability distribution of candidates, ultimately predicting the correct answer. We conduct experiments on three challenging open-ended VideoQA datasets, surpassing existing SoTA methods. Extensive experiments further explore and analyse the impact of each modules, as well as the design of diffusion model, demonstrating the effectiveness of DiffAns. Our code is available at https://github.com/WanJJJh/DiffAns.

Multi-label intent detection aims to recognize multiple user intents behind dialogue utterances. The diversity of user utterances and the scarcity of training data motivate multi-label few-shot intent detection. However, existing methods ignore the hybrid of verb and noun within an intent, which is essential to identify the user intent. In this paper, we propose a label hierarchical structure-aware method for multi-label few-shot intent detection via prompt tuning (LHS). Firstly, for the support data, we concatenate the original utterance with the label description generated by GPT-4 to obtain the utterance-level representation. Then we construct a multi-label hierarchical structure-aware prompt model to learn the label hierarchical information. To learn more discriminative class prototypes, we devise a prototypical contrastive learning method to pull the utterances close to their corresponding intent labels and away from other intent labels. Extensive experiments on two datasets demonstrate the superiority of our method.

Early depression detection is essential to enable healthcare professionals to effectively intervene and treat the depressive conditions. In existing research, an increasing number of psychological manifestations of depression are incorporated, and demonstrated effective by integrating them into computational models, particularly the emotional cues of depression. Although emotional factors are effective in depression detection, few studies have paid attention to the underlying depressive causal factors hidden within social media text for depression detection. In this paper, we propose a novel partitioned filtering network model to extract emotional and causal features for predicting the depressive users on social media. Experimental results demonstrate the proposed model achieves superior performance over recent baseline models on the dataset by highlighting the emotional and causal factors.

Emotion recognition in conversation (ERC) enables a deeper understanding of emotion for each utterance within a conversation. Recent progress on ERC has proved that using Graph Neural Networks (GNN) to model conversational context is effective for identifying emotions. However, existing GNN-based approaches still suffer from two limitations: (1) they model the context of each utterance with a certain window, which ignores the diversity of emotion changes of utterances in conversation; (2) they mostly take no account of additional knowledge information, which limits the performance of ERC. In this paper, we propose a knowledge-aware graph convo-lutional network (KGCN-ERC) by introducing a knowledge graph into node connection of graph neural networks for the first time. Based on the rich sentiment knowledge, KGCN-ERC searches for the most appropriate local window for each utterance and builds sensible utterance connections. Experiments show that our approach achieves competitive performance compared with state-of-the-art ERC methods.

Few-shot learning is the key step towards human-level intelligence. Prototypical Network is a promising approach to address the key issue of over-fitting for few-shot learning. Nevertheless, the original Prototypical Network only uses one or few labeled instances to represent the corresponding class, which easily deviates from the real class distribution leading to the imprecise classification results. In this paper, we propose Transductive Prototypical Network (Td-PN), a universal transductive approach that refines the class representations by merging scarce labeled samples and high-confidence ones of target set. Our proposed Td-PN first maps the samples to a classifying-friendly (discriminative) embedding space by redesigning a weighted contrastive loss, then utilizes the transductive inference to obtain the powerful prototype representation for each class. Experiments demonstrate that our approach outperforms the state-of-the-art algorithms.

Deep spectral clustering networks have shown their superiorities due to the integration of feature learning and cluster assignment, and the ability to deal with non-convex clusters. Nevertheless, deep spectral clustering is still an ill-posed problem. Specifically, the affinity learned by the most remarkable SpectralNet is not guaranteed to be consistent with local invariance and thus hurts the final clustering performance. In this paper, we propose a novel framework of Constrained Spectral Clustering Network (CSCN) by incorporating pairwise constraints and clustering oriented fine-tuning to deal with the ill-posedness. To the best of our knowledge, this is the first constrained deep spectral clustering method. Another advantage of CSCN over existing constrained deep clustering networks is that it propagates pairwise constraints throughout the entire dataset. In addition, we design a clustering oriented loss by self-training to simultaneously finetune feature representations and perform cluster assignments, which further improve the quality of clustering. Extensive experiments on benchmark datasets demonstrate that our approach outperforms the state-of-the-art clustering methods.

In dynamic networks, links are annotated with timestamps showing the emerging time and the link prediction problem is to infer the future links in networks. Universal link prediction methods are highly demanded in various applications, which require universal link features that are feasible for multiple kinds of network topological structures and capable to address the difference of links with different timestamps. In this paper, we propose a novel link feature called Structure Subgraph Feature (SSF). The SSF is an outstanding link feature that is feasible to various dynamic networks due to the following superiorities: (1) the proposed structure subgraph is so far the most effective manner to represent surrounding topological features of target link and (2) the normalized influence well specifies the influence of multiple links and different timestamps in structure subgraph. We finally propose two link prediction methods by applying SSF to a linear regression model and a neural machine. Experimental results on real-world dynamic network datasets indicate that the SSF-based methods consistently provide top-class performance on various dynamic networks.

Considering the diversity of the views, assigning the multiviews with different weights is important to multi-view clustering. Several multi-view clustering algorithms have been proposed to assign different weights to the views. However, the existing weighting schemes do not simultaneously consider the characteristic of multi-view clustering and the characteristic of related single-view clustering. In this paper, based on the spectral perturbation theory of spectral clustering, we propose a weighted multi-view spectral clustering algorithm which employs the spectral perturbation to model the weights of the views. The proposed weighting scheme follows the two basic principles: 1) the clustering results on each view should be close to the consensus clustering result, and 2) views with similar clustering results should be assigned similar weights. According to spectral perturbation theory, the largest canonical angle is used to measure the difference between spectral clustering results. In this way, the weighting scheme can be formulated into a standard quadratic programming problem. Experimental results demonstrate the superiority of the proposed algorithm.

Multi-task clustering improves the clustering performance of each task by transferring knowledge across related tasks. Most existing multi-task clustering methods are based on the ideal assumption that the tasks are completely related. However, in real applications, the tasks are usually partially related. In these cases, brute-force transfer may cause negative effect which degrades the clustering performance. In this paper, we propose two multi-task clustering methods for partially related tasks: the self-adapted multi-task clustering (SAMTC) method and the manifold regularized coding multi-task clustering (MRCMTC) method, which can automatically identify and transfer related instances among the tasks, thus avoiding negative transfer. Both SAMTC and MRCMTC construct the similarity matrix for each target task by exploiting useful information from the source tasks through related instances transfer, and adopt spectral clustering to get the final clustering results. But, they learn the related instances from the source tasks in different ways. Experimental results on real data sets show the superiorities of the proposed algorithms over traditional single-task clustering methods and existing multi-task clustering methods on both completely and partially related tasks.

Multi-task clustering and multi-view clustering have severally found wide applications and received much attention in recent years. Nevertheless, there are many clustering problems that involve both multi-task clustering and multi-view clustering, i.e., the tasks are closely related and each task can be analyzed from multiple views. In this paper, we introduce a multi-task multi-view clustering framework which integrates within-view-task clustering, multi-view relationship learning, and multi-task relationship learning. Under this framework, we propose two multi-task multi-view clustering algorithms, the bipartite graph based multi-task multi-view clustering algorithm, and the semi-nonnegative matrix tri-factorization based multi-task multi-view clustering algorithm. The former one can deal with the multi-task multi-view clustering of nonnegative data, the latter one is a general multi-task multi-view clustering method, i.e., it can deal with the data with negative feature values. Experimental results on publicly available data sets in web page mining and image mining show the superiority of the proposed multi-task multi-view clustering algorithms over either multi-task clustering algorithms or multi-view clustering algorithms for multi-task clustering of multi-view data.

Nonnegative matrix factorization (NMF) and symmetric NMF (SymNMF) have been shown to be effective for clustering linearly separable data and nonlinearly separable data, respectively. Nevertheless, many practical applications demand constrained algorithms in which a small number of constraints in the form of must-link and cannot-link are available. In this paper, we propose an NMF-based constrained clustering framework in which the similarity between two points on a must-link is enforced to approximate 1 and the similarity between two points on a cannot-link is enforced to approximate 0. We then formulate the framework using NMF and SymNMF to deal with clustering of linearly separable data and nonlinearly separable data, respectively. Furthermore, we present multiplicative update rules to solve them and show the correctness and convergence. Experimental results on various text data sets, University of California, Irvine (UCI) data sets, and gene expression data sets demonstrate the superiority of our algorithms over existing constrained clustering algorithms.

Existing multi-view clustering algorithms require thatthe data is completely or partially mapped betweeneach pair of views. However, this requirement couldnot be satisfied in most practical settings. In this paper,we tackle the problem of multi-view clustering for unmappeddata in the framework of NMF based clustering.With the help of inter-view constraints, we definethe disagreement between each pair of views by the factthat the indicator vectors of two instances from two differentviews should be similar if they belong to the samecluster and dissimilar otherwise. The overall objectiveof our algorithm is to minimize the loss function of NMFin each view as well as the disagreement betweeneach pair of views. Experimental results show that, witha small number of constraints, the proposed algorithmgets good performance on unmapped data, and outperformsexisting algorithms on partially mapped data andcompletely mapped data.

Density-based techniques seem promising for handling datauncertainty in uncertain data clustering. Nevertheless, someissues have not been addressed well in existing algorithms. Inthis paper, we firstly propose a novel density-based uncertaindata clustering algorithm, which improves upon existing algorithmsfrom the following two aspects: (1) it employs anexact method to compute the probability that the distance betweentwo uncertain objects is less than or equal to a boundaryvalue, instead of the sampling-based method in previouswork; (2) it introduces new definitions of core object probabilityand direct reachability probability, thus reducing thecomplexity and avoiding sampling. We then further improvethe algorithm by using a novel assignment strategy to ensurethat every object will be assigned to the most appropriatecluster. Experimental results show the superiority of our proposedalgorithms over existing ones.

Multi-view clustering integrates complementary information from multiple views to gain better clustering performance rather than relying on a single view. NMF based multi-view clustering algorithms have shown their competitiveness among different multi-view clustering algorithms. However, NMF fails to preserve the locally geometrical structure of the data space. In this paper, we propose a multi-manifold regularized nonnegative matrix factorization framework (MMNMF) which can preserve the locally geometrical structure of the manifolds for multi-view clustering. MMNMF regards that the intrinsic manifold of the dataset is embedded in a convex hull of all the views' manifolds, and incorporates such an intrinsic manifold and an intrinsic (consistent) coefficient matrix with a multi-manifold regularizer to preserve the locally geometrical structure of the multi-view data space. We use linear combination to construct the intrinsic manifold, and propose two strategies to find the intrinsic coefficient matrix, which lead to two instances of the framework. Experimental results show that the proposed algorithms outperform existing NMF based algorithms for multi-view clustering.