Subspace-Contrastive Multi-View Clustering

• URL: http://arxiv.org/abs/2210.06795v1
• Date: Thu, 13 Oct 2022 07:19:37 GMT
• Title: Subspace-Contrastive Multi-View Clustering
• Authors: Fu Lele, Zhang Lei, Yang Jinghua, Chen Chuan, Zhang Chuanfu, Zheng Zibin
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most multi-view clustering methods are limited by shallow models without sound nonlinear information perception capability, or fail to effectively exploit complementary information hidden in different views. To tackle these issues, we propose a novel Subspace-Contrastive Multi-View Clustering (SCMC) approach. Specifically, SCMC utilizes view-specific auto-encoders to map the original multi-view data into compact features perceiving its nonlinear structures. Considering the large semantic gap of data from different modalities, we employ subspace learning to unify the multi-view data into a joint semantic space, namely the embedded compact features are passed through multiple self-expression layers to learn the subspace representations, respectively. In order to enhance the discriminability and efficiently excavate the complementarity of various subspace representations, we use the contrastive strategy to maximize the similarity between positive pairs while differentiate negative pairs. Thus, a weighted fusion scheme is developed to initially learn a consistent affinity matrix. Furthermore, we employ the graph regularization to encode the local geometric structure within varying subspaces for further fine-tuning the appropriate affinities between instances. To demonstrate the effectiveness of the proposed model, we conduct a large number of comparative experiments on eight challenge datasets, the experimental results show that SCMC outperforms existing shallow and deep multi-view clustering methods.

Related papers

• Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein [56.62376364594194]
  Unsupervised learning aims to capture the underlying structure of potentially large and high-dimensional datasets. In this work, we revisit these approaches under the lens of optimal transport and exhibit relationships with the Gromov-Wasserstein problem. This unveils a new general framework, called distributional reduction, that recovers DR and clustering as special cases and allows addressing them jointly within a single optimization problem.
  arXiv  Detail & Related papers  (2024-02-03T19:00:19Z)
• One for all: A novel Dual-space Co-training baseline for Large-scale Multi-View Clustering [42.92751228313385]
  We propose a novel multi-view clustering model, named Dual-space Co-training Large-scale Multi-view Clustering (DSCMC) The main objective of our approach is to enhance the clustering performance by leveraging co-training in two distinct spaces. Our algorithm has an approximate linear computational complexity, which guarantees its successful application on large-scale datasets.
  arXiv  Detail & Related papers  (2024-01-28T16:30:13Z)
• Efficient and Effective Deep Multi-view Subspace Clustering [9.6753782215283]
  We propose a novel deep framework, termed Efficient and Effective deep Multi-View Subspace Clustering (E$2$MVSC) Instead of a parameterized FC layer, we design a Relation-Metric Net that decouples network parameter scale from sample numbers for greater computational efficiency. E$2$MVSC yields comparable results to existing methods and achieves state-of-the-art performance in various types of multi-view datasets.
  arXiv  Detail & Related papers  (2023-10-15T03:08:25Z)
• Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information [28.300395619444796]
  In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot. We propose a novel multi-view clustering network termed CodingNet to explore the diverse and consistent information simultaneously. Our framework's efficacy is validated through extensive experiments on six widely used benchmark datasets.
  arXiv  Detail & Related papers  (2023-09-01T14:13:22Z)
• Multi-View Clustering via Semi-non-negative Tensor Factorization [120.87318230985653]
  We develop a novel multi-view clustering based on semi-non-negative tensor factorization (Semi-NTF) Our model directly considers the between-view relationship and exploits the between-view complementary information. In addition, we provide an optimization algorithm for the proposed method and prove mathematically that the algorithm always converges to the stationary KKT point.
  arXiv  Detail & Related papers  (2023-03-29T14:54:19Z)
• Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework [74.25493157757943]
  We formulate a novel clustering model, which exploits the non-negative feature property and incorporates the multi-view information into a unified joint learning framework. We also explore, for the first time, the multi-model non-negative graph-based approach to clustering data based on deep features.
  arXiv  Detail & Related papers  (2022-11-03T08:18:27Z)
• 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)
• 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)
• 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)

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.