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.
@article{arxiv.1905.04432,
title = {Joint Learning of Self-Representation and Indicator for Multi-View Image Clustering},
author = {Songsong Wu and Zhiqiang Lu and Hao Tang and Yan Yan and Songhao Zhu and Xiao-Yuan Jing and Zuoyong Li},
journal= {arXiv preprint arXiv:1905.04432},
year = {2019}
}