Enhanced Latent Multi-view Subspace Clustering

• URL: http://arxiv.org/abs/2312.14763v2
• Date: Tue, 27 Aug 2024 05:09:09 GMT
• Title: Enhanced Latent Multi-view Subspace Clustering
• Authors: Long Shi, Lei Cao, Jun Wang, Badong Chen,
• Abstract summary: We propose an Enhanced Latent Multi-view Subspace Clustering (ELMSC) method for recovering latent space representation. Our proposed ELMSC is able to achieve higher clustering performance than some state-of-art multi-view clustering methods.
• Score: 25.343388834470247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Latent multi-view subspace clustering has been demonstrated to have desirable clustering performance. However, the original latent representation method vertically concatenates the data matrices from multiple views into a single matrix along the direction of dimensionality to recover the latent representation matrix, which may result in an incomplete information recovery. To fully recover the latent space representation, we in this paper propose an Enhanced Latent Multi-view Subspace Clustering (ELMSC) method. The ELMSC method involves constructing an augmented data matrix that enhances the representation of multi-view data. Specifically, we stack the data matrices from various views into the block-diagonal locations of the augmented matrix to exploit the complementary information. Meanwhile, the non-block-diagonal entries are composed based on the similarity between different views to capture the consistent information. In addition, we enforce a sparse regularization for the non-diagonal blocks of the augmented self-representation matrix to avoid redundant calculations of consistency information. Finally, a novel iterative algorithm based on the framework of Alternating Direction Method of Multipliers (ADMM) is developed to solve the optimization problem for ELMSC. Extensive experiments on real-world datasets demonstrate that our proposed ELMSC is able to achieve higher clustering performance than some state-of-art multi-view clustering methods.

Related papers

• One-step Multi-view Clustering with Diverse Representation [47.41455937479201]
  We propose a one-step multi-view clustering with diverse representation method, which incorporates multi-view learning and $k$-means into a unified framework. We develop an efficient optimization algorithm with proven convergence to solve the resultant problem.
  arXiv  Detail & Related papers  (2023-06-08T02:52:24Z)
• 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)
• Late Fusion Multi-view Clustering via Global and Local Alignment Maximization [61.89218392703043]
  Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Most of existing approaches directly fuse multiple pre-specified similarities to learn an optimal similarity matrix for clustering. We propose late fusion MVC via alignment to address these issues.
  arXiv  Detail & Related papers  (2022-08-02T01:49:31Z)
• Multi-view Clustering via Deep Matrix Factorization and Partition Alignment [43.56334737599984]
  We propose a novel multi-view clustering algorithm via deep matrix decomposition and partition alignment. An alternating optimization algorithm is developed to solve the optimization problem with proven convergence.
  arXiv  Detail & Related papers  (2021-05-01T15:06:57Z)
• Multi-view Clustering with Deep Matrix Factorization and Global Graph Refinement [37.34296330445708]
  Multi-view clustering is an important yet challenging task in machine learning and data mining. We propose a novel Multi-View Clustering method with Deep semi-NMF and Global Graph Refinement (MVC-DMF-GGR) in this paper.
  arXiv  Detail & Related papers  (2021-05-01T13:40:20Z)
• Discriminatively Constrained Semi-supervised Multi-view Nonnegative Matrix Factorization with Graph Regularization [10.978930376656423]
  We propose a novel Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization (DCS2MVNMF) Specifically, a discriminative weighting matrix is introduced for the auxiliary matrix of each view, which enhances the inter-class distinction. In addition, we design a new feature scale normalization strategy to align the multiple views and complete the corresponding iterative optimization schemes.
  arXiv  Detail & Related papers  (2020-10-26T02:58:11Z)
• Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix [57.11971786407279]
  Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data. This paper proposes a multi-view spectral clustering algorithm that learns a high-order optimal neighborhood Laplacian matrix. Our proposed algorithm generates the optimal Laplacian matrix by searching the neighborhood of the linear combination of both the first-order and high-order base.
  arXiv  Detail & Related papers  (2020-08-31T12:28:40Z)
• 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)
• Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering [50.43424130281065]
  We propose a new Semi-Nonnegative Matrix Factorization method for 2-dimensional (2D) data, named TS-NMF. It overcomes the drawback of existing methods that seriously damage the spatial information of the data by converting 2D data to vectors in a preprocessing step.
  arXiv  Detail & Related papers  (2020-05-19T05:54:14Z)

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.