S^2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering

• URL: http://arxiv.org/abs/2403.09107v2
• Date: Thu, 11 Apr 2024 07:42:43 GMT
• Title: S^2MVTC: a Simple yet Efficient Scalable Multi-View Tensor Clustering
• Authors: Zhen Long, Qiyuan Wang, Yazhou Ren, Yipeng Liu, Ce Zhu,
• Abstract summary: Experimental results on six large-scale multi-view datasets demonstrate that S2MVTC significantly outperforms state-of-the-art algorithms in terms of clustering performance and CPU execution time.
• Score: 38.35594663863098
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anchor-based large-scale multi-view clustering has attracted considerable attention for its effectiveness in handling massive datasets. However, current methods mainly seek the consensus embedding feature for clustering by exploring global correlations between anchor graphs or projection matrices.In this paper, we propose a simple yet efficient scalable multi-view tensor clustering (S^2MVTC) approach, where our focus is on learning correlations of embedding features within and across views. Specifically, we first construct the embedding feature tensor by stacking the embedding features of different views into a tensor and rotating it. Additionally, we build a novel tensor low-frequency approximation (TLFA) operator, which incorporates graph similarity into embedding feature learning, efficiently achieving smooth representation of embedding features within different views. Furthermore, consensus constraints are applied to embedding features to ensure inter-view semantic consistency. Experimental results on six large-scale multi-view datasets demonstrate that S^2MVTC significantly outperforms state-of-the-art algorithms in terms of clustering performance and CPU execution time, especially when handling massive data. The code of S^2MVTC is publicly available at https://github.com/longzhen520/S2MVTC.

Related papers

• Fast Disentangled Slim Tensor Learning for Multi-view Clustering [28.950845031752927]
  We propose a new approach termed fast Disdentangle Slim Learning (DSTL) for multi-view clustering. To alleviate the negative influence of feature redundancy, inspired by robust PCA, DSTL disentangles the latent low-dimensional representation into a semantic-unrelated part and a semantic-related part for each view. Our proposed model is computationally efficient and can be solved effectively.
  arXiv  Detail & Related papers  (2024-11-12T09:57:53Z)
• Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization [64.00146569922028]
  Multi-view clustering methods based on anchor graph factorization lack adequate cluster interpretability for the decomposed matrix. We address this limitation by using non-negative tensor factorization to decompose an anchor graph tensor that combines anchor graphs from multiple views.
  arXiv  Detail & Related papers  (2024-04-01T03:23:55Z)
• 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)
• 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)
• Multi-view MERA Subspace Clustering [42.33688860165733]
  Multi-view subspace clustering (MSC) can capture high-order correlation in the self-representation tensor. We propose a low-rank MERA based MSC (MERA-MSC) algorithm, where MERA factorizes a tensor into contractions of one top core factor and the rest orthogonal/semi-orthogonal factors. We extend MERA-MSC by incorporating anchor learning to develop a scalable low-rank MERA based multi-view clustering method (sMREA-MVC)
  arXiv  Detail & Related papers  (2023-05-16T01:41:10Z)
• Deep Multi-View Subspace Clustering with Anchor Graph [11.291831842959926]
  We propose a novel deep multi-view subspace clustering method with anchor graph (DMCAG) DMCAG learns the embedded features for each view independently, which are used to obtain the subspace representations. Our method achieves superior clustering performance over other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-11T16:17:43Z)
• 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)
• 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)
• Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
  We propose an effective and efficient graph learning model for multi-view clustering. Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
  arXiv  Detail & Related papers  (2021-08-15T13:14:28Z)
• Smoothed Multi-View Subspace Clustering [14.77544837600836]
  We propose a novel multi-view clustering method named smoothed multi-view subspace clustering (SMVSC) It employs a novel technique, i.e., graph filtering, to obtain a smooth representation for each view. Experiments on benchmark datasets validate the superiority of our approach.
  arXiv  Detail & Related papers  (2021-06-18T02:24:19Z)

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.