Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences

• URL: http://arxiv.org/abs/2205.15075v1
• Date: Mon, 30 May 2022 13:07:40 GMT
• Title: Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences
• Authors: Siwei Wang, Xinwang Liu, Suyuan Liu, Jiaqi Jin, Wenxuan Tu, Xinzhong Zhu, En Zhu
• Abstract summary: Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities. Existing approaches do not pay sufficient attention to establishing correct correspondences between the anchor sets across views.
• Score: 53.09276639185084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities and therefore reduce the complexity of graph methods. Although widely applied in large-scale applications, existing approaches do not pay sufficient attention to establishing correct correspondences between the anchor sets across views. To be specific, anchor graphs obtained from different views are not aligned column-wisely. Such an Anchor-Unaligned Problem (AUP) would cause inaccurate graph fusion and degrade the clustering performance. Under multi-view scenarios, generating correct correspondences could be extremely difficult since anchors are not consistent in feature dimensions. To solve this challenging issue, we propose the first study of a generalized and flexible anchor graph fusion framework termed Fast Multi-View Anchor-Correspondence Clustering (FMVACC). Specifically, we show how to find anchor correspondence with both feature and structure information, after which anchor graph fusion is performed column-wisely. Moreover, we theoretically show the connection between FMVACC and existing multi-view late fusion and partial view-aligned clustering, which further demonstrates our generality. Extensive experiments on seven benchmark datasets demonstrate the effectiveness and efficiency of our proposed method. Moreover, the proposed alignment module also shows significant performance improvement applying to existing multi-view anchor graph competitors indicating the importance of anchor alignment.

Related papers

• 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)
• Scalable Incomplete Multi-View Clustering with Structure Alignment [71.62781659121092]
  In this paper, we propose a novel incomplete anchor graph learning framework. We construct the view-specific anchor graph to capture the complementary information from different views. The time and space complexity of the proposed SIMVC-SA is proven to be linearly correlated with the number of samples.
  arXiv  Detail & Related papers  (2023-08-31T08:30:26Z)
• Unpaired Multi-View Graph Clustering with Cross-View Structure Matching [39.310384044597065]
  Most existing MVC methods assume that multi-view data are fully paired, which means that the mappings of all corresponding samples between views are pre-defined or given in advance. The data correspondence is often incomplete in real-world applications due to data corruption or sensor differences. We propose a novel parameter-free graph clustering framework termed Unpaired Multi-view Graph Clustering framework with Cross-View Structure Matching.
  arXiv  Detail & Related papers  (2023-07-07T09:29:44Z)
• Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID [56.573905143954015]
  We propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters. Under such a supervisory signal, a Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework is proposed to align features jointly at a cluster-level. Experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2023-05-22T03:27:46Z)
• A Clustering-guided Contrastive Fusion for Multi-view Representation Learning [7.630965478083513]
  We propose a deep fusion network to fuse view-specific representations into the view-common representation. We also design an asymmetrical contrastive strategy that aligns the view-common representation and each view-specific representation. In the incomplete view scenario, our proposed method resists noise interference better than those of our competitors.
  arXiv  Detail & Related papers  (2022-12-28T07:21:05Z)
• 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)
• Consistent and Complementary Graph Regularized Multi-view Subspace Clustering [31.187031653119025]
  This study investigates the problem of multi-view clustering, where multiple views contain consistent information and each view also includes complementary information. We propose a method that involves consistent and complementary graph-regularized multi-view subspace clustering (GRMSC) The objective function is optimized by the augmented Lagrangian multiplier method in order to achieve multi-view clustering.
  arXiv  Detail & Related papers  (2020-04-07T03:48:08Z)
• Learning to Cluster Faces via Confidence and Connectivity Estimation [136.5291151775236]
  We propose a fully learnable clustering framework without requiring a large number of overlapped subgraphs. Our method significantly improves clustering accuracy and thus performance of the recognition models trained on top, yet it is an order of magnitude more efficient than existing supervised methods.
  arXiv  Detail & Related papers  (2020-04-01T13:39:37Z)

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.