Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering

• URL: http://arxiv.org/abs/2203.08060v1
• Date: Tue, 15 Mar 2022 16:52:15 GMT
• Title: Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering
• Authors: Xiaosha Cai, Dong Huang, Guang-Yu Zhang, Chang-Dong Wang
• Abstract summary: We propose a jointly smoothed multi-view subspace clustering (JSMC) approach. Specifically, we incorporate the cross-view commonness and inconsistencies into the subspace representation learning. Experimental results on a variety of real-world multi-view datasets have confirmed the superiority of the proposed approach.
• Score: 18.73046476758598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-view subspace clustering aims to discover the hidden subspace structures from multiple views for robust clustering, and has been attracting considerable attention in recent years. Despite significant progress, most of the previous multi-view subspace clustering algorithms are still faced with two limitations. First, they usually focus on the consistency (or commonness) of multiple views, yet often lack the ability to capture the cross-view inconsistencies in subspace representations. Second, many of them overlook the local structures of multiple views and cannot jointly leverage multiple local structures to enhance the subspace representation learning. To address these two limitations, in this paper, we propose a jointly smoothed multi-view subspace clustering (JSMC) approach. Specifically, we simultaneously incorporate the cross-view commonness and inconsistencies into the subspace representation learning. The view-consensus grouping effect is presented to jointly exploit the local structures of multiple views to regularize the view-commonness representation, which is further associated with the low-rank constraint via the nuclear norm to strengthen its cluster structure. Thus the cross-view commonness and inconsistencies, the view-consensus grouping effect, and the low-rank representation are seamlessly incorporated into a unified objective function, upon which an alternating optimization algorithm is performed to achieve a robust subspace representation for clustering. Experimental results on a variety of real-world multi-view datasets have confirmed the superiority of the proposed approach.

Related papers

• Hierarchical Mutual Information Analysis: Towards Multi-view Clustering in The Wild [9.380271109354474]
  This work proposes a deep MVC framework where data recovery and alignment are fused in a hierarchically consistent way to maximize the mutual information among different views. To the best of our knowledge, this could be the first successful attempt to handle the missing and unaligned data problem separately with different learning paradigms.
  arXiv  Detail & Related papers  (2023-10-28T06:43:57Z)
• Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent [46.86939432189035]
  We propose Anchor-based Multi-view Subspace Clustering with Hierarchical Feature Descent. Our proposed model consistently outperforms the state-of-the-art techniques.
  arXiv  Detail & Related papers  (2023-10-11T03:29:13Z)
• DealMVC: Dual Contrastive Calibration for Multi-view Clustering [78.54355167448614]
  We propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC) We first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph. During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels.
  arXiv  Detail & Related papers  (2023-08-17T14:14:28Z)
• Subspace-Contrastive Multi-View Clustering [0.0]
  We propose a novel Subspace-Contrastive Multi-View Clustering (SCMC) approach. We employ view-specific auto-encoders to map the original multi-view data into compact features perceiving its nonlinear structures. To demonstrate the effectiveness of the proposed model, we conduct a large number of comparative experiments on eight challenge datasets.
  arXiv  Detail & Related papers  (2022-10-13T07:19:37Z)
• Adaptively-weighted Integral Space for Fast Multiview Clustering [54.177846260063966]
  We propose an Adaptively-weighted Integral Space for Fast Multiview Clustering (AIMC) with nearly linear complexity. Specifically, view generation models are designed to reconstruct the view observations from the latent integral space. Experiments conducted on several realworld datasets confirm the superiority of the proposed AIMC method.
  arXiv  Detail & Related papers  (2022-08-25T05:47:39Z)
• Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity [63.85428043085567]
  We propose a fast multi-view clustering via ensembles (FastMICE) approach. The concept of random view groups is presented to capture the versatile view-wise relationships. FastMICE has almost linear time and space complexity, and is free of dataset-specific tuning.
  arXiv  Detail & Related papers  (2022-03-22T09:51:24Z)
• 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)
• Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering [3.093890460224435]
  Multi-view clustering integrates multiple feature sets, which reveal distinct aspects of the data and provide complementary information to each other. Most existing multi-view clustering methods only aim to explore the consistency of all views while ignoring the local structure of each view. We propose a novel multi-view subspace clustering method via simultaneously assigning weights for different features and capturing local information of data in view-specific self-representation feature spaces.
  arXiv  Detail & Related papers  (2020-07-25T01:57:57Z)
• 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)

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.