Deep multi-modal clustering fully learns semantically consistent and discriminative cluster representations between multiple modalities in an unlabeled manner. However, existing methods treat all samples equally, ignoring varying sample quality, which limits clustering performance. Inspired by the concept of interest in the recommendation system, we propose a novel interest-driven deep multi-modal clustering (IDMC) framework. It designs a new paradigm to quantify the importance of each sample base on the attention it receives from other samples, which called interest value. This value jointly captures the local geometric structure through the Euclidean distance in feature space and the consistency of pseudo-labels. Then, we design a novel adaptive Bayesian fusion mechanism to dynamically balance the prior features and self-supervisory signals to ensure confidence-based sample importance estimation. Furthermore, we introduce a median normalization constraint and a label consistency constraint to further refine the construction of the interest value. By embedding this interest-guided value into representation learning and cluster optimization, IDMC focuses on the samples with the most information and the most stable semantics, thereby enhancing the performance of multi-modal representation learning. Extensive experiments verify that IDMC is superior to existing state-of-the-art methods in multiple evaluation metrics.

Deep multi-modal clustering can extract useful information among modals, thus benefiting the final clustering and many related fields. However, existing multi-modal clustering methods have two major limitations. First, they often ignore different levels of guiding information from both the feature representations and cluster assignments, which thus are difficult in learning discriminative representations. Second, most methods fail to effectively eliminate redundant information between multi-modal data, negatively affecting clustering results. In this paper, we propose a novel multi-aspect self-guided deep information bottleneck (MSDIB) method for multi-modal clustering, which can effectively employ different aspects of guiding information for learning cluster-friendly information among modals. MSDIB mainly contains two parts: information compression and information preservation. In information compression, we extract from the private information of each modality to obtain the compact representation and meanwhile conduct mutual compression between them. In information preservation, the aim is to preserve the shared information among modals and the self-supervised information from the clustering results in each iteration. In the above process, there are mainly three aspects of self-guiding information, the modality-private information, the modality-shared information and the self-supervised pseudo label information. By minimizing the mutual information based objective function with a variational optimization method, we can fully extract useful discriminative information while eliminating the irrelevant parts. Extensive experimental results demonstrate that our method outperforms state-of-the-art multi-modal clustering methods, showcasing its superior performance and broad application prospects.

Deep multiview clustering (MVC) is to learn and utilize the rich relations across different views to enhance the clustering performance under a human-designed deep network. However, most existing deep MVCs meet two challenges. First, most current deep contrastive MVCs usually select the same instance across views as positive pairs and the remaining instances as negative pairs, which always leads to inaccurate contrastive learning (CL). Second, most deep MVCs only consider learning feature or cluster correlations across views, failing to explore the dual correlations. To tackle the above challenges, in this article, we propose a novel deep MVC framework by pseudo-label guided CL and dual correlation learning. Specifically, a novel pseudo-label guided CL mechanism is designed by using the pseudo-labels in each iteration to help removing false negative sample pairs, so that the CL for the feature distribution alignment can be more accurate, thus benefiting the discriminative feature learning. Different from most deep MVCs learning only one kind of correlation, we investigate both the feature and cluster correlations among views to discover the rich and comprehensive relations. Experiments on various datasets demonstrate the superiority of our method over many state-of-the-art compared deep MVCs. The source implementation code will be provided at https://github.com/ShizheHu/Deep-MVC-PGCL-DCL.

Contrastive multi-view clustering is widely recognized for its effectiveness in mining feature representation across views via contrastive learning (CL), gaining significant attention in recent years. Most existing methods mainly focus on the feature-level or/and cluster-level CL, but there are still two shortcomings. Firstly, feature-level CL is limited by the influence of anomalies and large noise data, resulting in insufficient mining of discriminative feature representation. Secondly, cluster-level CL lacks the guidance of global information and is always restricted by the local diversity information. We in this paper Learn dUal enhanCed rEpresentation for Contrastive Multi-view Clustering (LUCE-CMC) to effectively addresses the above challenges, and it mainly contains two parts, i.e., enhanced feature-level CL (En-FeaCL) and enhanced cluster-level CL (En-CluCL). Specifically, we first adopt a shared encoder to learn shared feature representations between multiple views and then obtain cluster-relevant information that is beneficial to the clustering results. Moreover, we design a reconstitution approach to force the model to concentrate on learning features that are critical to reconstructing the input data, reducing the impact of noisy data and maximizing the sufficient discriminative information of different views in helping the En-FeaCL part. Finally, instead of contrasting the view-specific clustering result like most existing methods do, we in the En-CluCL part make the information at the cluster-level more richer by contrasting the cluster assignment from each view and the cluster assignment obtained from the shared fused features. The end-to-end training methods of the proposed model are mutually reinforcing and beneficial. Extensive experiments conducted on multi-view datasets show that the proposed LUCE-CMC outperforms established baselines to a considerable extent. The source code is released at https://github.com/ShizheHu.

With the fast-growing multi-modal/media data in the Big Data era, multi-view clustering (MVC) has attracted lots of attentions lately. Most MVCs focus on integrating and utilizing the complementary information among views by linear sum of the learned view weights and have shown great success in some fields. However, they fail to quantify how complementary the information across views actually utilized for benefiting final clustering. Additionally, most of them contain at least one parameter for regularization without prior knowledge, which puts pressure on the parameter-tuning and thus makes them impractical. In this paper, we propose a novel parameter-free multi-view information bottleneck (PMIB) clustering method to automatically identify and exploit useful complementary information among views, thus reducing the negative impact from the harmful views. Specifically, we first discover the informative view by measuring the relevant information preserved by the original data and the compact clusters with mutual information. Then, a new cross-view weight learning scheme is designed to learn how complementary between the informative view and remaining views. Finally, the quantitative correlations among views are fully exploited to improve the clustering performance without needing any additional parameters or prior knowledge. Experimental results on different kinds of multi-view datasets show the effectiveness of the proposed method.