Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering

• URL: http://arxiv.org/abs/2204.12496v1
• Date: Tue, 26 Apr 2022 15:49:59 GMT
• Title: Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering
• Authors: Shiye Wang, Changsheng Li, Yanming Li, Ye Yuan, Guoren Wang
• Abstract summary: We establish a new framework called Self-supervised Information Bottleneck based Multi-view Subspace Clustering (SIB-MSC) Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views. Our method achieves superior performance over the related state-of-the-art methods.
• Score: 29.27475285925792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we explore the problem of deep multi-view subspace clustering framework from an information-theoretic point of view. We extend the traditional information bottleneck principle to learn common information among different views in a self-supervised manner, and accordingly establish a new framework called Self-supervised Information Bottleneck based Multi-view Subspace Clustering (SIB-MSC). Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representations of other views. Actually, the latent representation of each view provides a kind of self-supervised signal for training the latent representations of other views. Moreover, SIB-MSC attempts to learn the other latent space for each view to capture the view-specific information by introducing mutual information based regularization terms, so as to further improve the performance of multi-view subspace clustering. To the best of our knowledge, this is the first work to explore information bottleneck for multi-view subspace clustering. Extensive experiments on real-world multi-view data demonstrate that our method achieves superior performance over the related state-of-the-art methods.

Related papers

• Multi-View Class Incremental Learning [57.14644913531313]
  Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. This paper investigates a novel paradigm called multi-view class incremental learning (MVCIL), where a single model incrementally classifies new classes from a continual stream of views.
  arXiv  Detail & Related papers  (2023-06-16T08:13:41Z)
• Multi-view Semantic Consistency based Information Bottleneck for Clustering [13.589996737740208]
  We introduce a novel Multi-view Semantic Consistency based Information Bottleneck for clustering (MSCIB) MSCIB pursues semantic consistency to improve the learning process of information bottleneck for different views. It conducts the alignment operation of multiple views in the semantic space and jointly achieves the valuable consistent information of multi-view data.
  arXiv  Detail & Related papers  (2023-02-28T02:01:58Z)
• Multi-View Clustering from the Perspective of Mutual Information [0.0]
  We propose a novel model based on information theory termed Informative Multi-View Clustering (IMVC) IMVC extracts the common and view-specific information hidden in multi-view data and constructs a clustering-oriented comprehensive representation. We conduct extensive experiments on six benchmark datasets, and the experimental results indicate that the proposed IMVC outperforms other methods.
  arXiv  Detail & Related papers  (2023-02-17T07:49:27Z)
• Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data [52.491074276133325]
  Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. We propose a new cross-view graph contrastive learning framework, which integrates multi-view information to align data and learn latent representations. Experiments conducted on several real datasets demonstrate the effectiveness of the proposed method on the clustering and classification tasks.
  arXiv  Detail & Related papers  (2022-11-08T09:19:32Z)
• Dual Representation Learning for One-Step Clustering of Multi-View Data [30.131568561100817]
  We propose a novel one-step multi-view clustering method by exploiting the dual representation of both the common and specific information of different views. With this framework, the representation learning and clustering partition mutually benefit each other, which effectively improve the clustering performance.
  arXiv  Detail & Related papers  (2022-08-30T14:20:26Z)
• Attentive Multi-View Deep Subspace Clustering Net [4.3386084277869505]
  We propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN) Our proposed method seeks to find a joint latent representation that explicitly considers both consensus and view-specific information. The experimental results on seven real-world data sets have demonstrated the effectiveness of our proposed algorithm against some state-of-the-art subspace learning approaches.
  arXiv  Detail & Related papers  (2021-12-23T12:57:26Z)
• Multi-view Subspace Clustering Networks with Local and Global Graph Information [19.64977233324484]
  The goal of this study is to explore the underlying grouping structure of data collected from different fields or measurements. We propose the novel multi-view subspace clustering networks with local and global graph information, termed MSCNLG. As an end-to-end trainable framework, the proposed method fully investigates the valuable information of multiple views.
  arXiv  Detail & Related papers  (2020-10-19T09:00:19Z)
• Collaborative Attention Mechanism for Multi-View Action Recognition [75.33062629093054]
  We propose a collaborative attention mechanism (CAM) for solving the multi-view action recognition problem. The proposed CAM detects the attention differences among multi-view, and adaptively integrates frame-level information to benefit each other. Experiments on four action datasets illustrate the proposed CAM achieves better results for each view and also boosts multi-view performance.
  arXiv  Detail & Related papers  (2020-09-14T17:33:10Z)
• Unsupervised Multi-view Clustering by Squeezing Hybrid Knowledge from Cross View and Each View [68.88732535086338]
  This paper proposes a new multi-view clustering method, low-rank subspace multi-view clustering based on adaptive graph regularization. Experimental results for five widely used multi-view benchmarks show that our proposed algorithm surpasses other state-of-the-art methods by a clear margin.
  arXiv  Detail & Related papers  (2020-08-23T08:25:06Z)
• Agglomerative Neural Networks for Multi-view Clustering [109.55325971050154]
  We propose the agglomerative analysis to approximate the optimal consensus view. We present Agglomerative Neural Network (ANN) based on Constrained Laplacian Rank to cluster multi-view data directly. Our evaluations against several state-of-the-art multi-view clustering approaches on four popular datasets show the promising view-consensus analysis ability of ANN.
  arXiv  Detail & Related papers  (2020-05-12T05:39:10Z)
• Generative Partial Multi-View Clustering [133.36721417531734]
  We propose a generative partial multi-view clustering model, named as GP-MVC, to address the incomplete multi-view problem. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the consistent cluster structure across multiple views. Second, view-specific generative adversarial networks are developed to generate the missing data of one view conditioning on the shared representation given by other views.
  arXiv  Detail & Related papers  (2020-03-29T17:48:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.