Deep low-rank tensor embedding for multi-view subspace clustering

Abstract

Despite the good clustering performance, most of existing multi-view subspace clustering methods fail to consider the non-linear relationships of high-dimensional data, higher-order correlations of different views, and optimal local geometric structure of the latent embedding space simultaneously. To this end, we put forward a novel multi-view subspace clustering model, named deep low-rank tensor embedding (DLTE). In DLTE, we project the high-dimensional data into the low-dimensional embedding space by utilizing deep non-negative matrix factorization (NMF), which can efficiently capture the complex non-linear relationship of data. Moreover, in the deep embedding space, we utilize the weighted low-rank tensor constraint to capture the global structure and higher-order correlations of different views while considering the contributions of different views. Additionally, we introduce an optimal graph Laplacian to learn a more reasonable local geometric structure of data in the deep embedding space. At last, comprehensive experimental results on eleven datasets indicate the superiority and effectiveness of the proposed DLTE method.