One for all: A novel Dual-space Co-training baseline for Large-scale Multi-View Clustering

• URL: http://arxiv.org/abs/2401.15691v1
• Date: Sun, 28 Jan 2024 16:30:13 GMT
• Title: One for all: A novel Dual-space Co-training baseline for Large-scale Multi-View Clustering
• Authors: Zisen Kong, Zhiqiang Fu, Dongxia Chang, Yiming Wang, Yao Zhao
• Abstract summary: 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.
• Score: 42.92751228313385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, 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. In the original space, we learn a projection matrix to obtain latent consistent anchor graphs from different views. This process involves capturing the inherent relationships and structures between data points within each view. Concurrently, we employ a feature transformation matrix to map samples from various views to a shared latent space. This transformation facilitates the alignment of information from multiple views, enabling a comprehensive understanding of the underlying data distribution. We jointly optimize the construction of the latent consistent anchor graph and the feature transformation to generate a discriminative anchor graph. This anchor graph effectively captures the essential characteristics of the multi-view data and serves as a reliable basis for subsequent clustering analysis. Moreover, the element-wise method is proposed to avoid the impact of diverse information between different views. Our algorithm has an approximate linear computational complexity, which guarantees its successful application on large-scale datasets. Through experimental validation, we demonstrate that our method significantly reduces computational complexity while yielding superior clustering performance compared to existing approaches.

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