Joint Learning of Self-Representation and Indicator for Multi-View Image Clustering
This work addresses multi-view image clustering, which is important for applications like computer vision and data analysis, but it appears incremental as it builds on existing spectral clustering approaches.
The paper tackles the problem of multi-view subspace clustering by proposing a unified model that jointly learns self-representation and cluster indicators, overcoming the limitation of separate learning in existing methods. Experimental results show that the method outperforms other competitive multi-view clustering methods on two benchmark datasets.
Multi-view subspace clustering aims to divide a set of multisource data into several groups according to their underlying subspace structure. Although the spectral clustering based methods achieve promotion in multi-view clustering, their utility is limited by the separate learning manner in which affinity matrix construction and cluster indicator estimation are isolated. In this paper, we propose to jointly learn the self-representation, continue and discrete cluster indicators in an unified model. Our model can explore the subspace structure of each view and fusion them to facilitate clustering simultaneously. Experimental results on two benchmark datasets demonstrate that our method outperforms other existing competitive multi-view clustering methods.