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Multi-View Subspace Clustering Based on Affinity Matrix and Low-rank Tensor Decomposition

Subspace clustering is one of the important methods to solve the problem of high-dimensional data clustering, and the performance of traditional methods based on spectral clustering largely depends on the affinity matrix learned directly from the original data or their computational representation. In this paper, we propose multi-view subspace clustering based on affinity matrix and low-rank tensor decomposition (ATMSC) algorithm, which not only constructs a more meaningful affinity matrix but also makes full use of multi-view complementary information and explores high-order correlation. The Alternating Direction Multiplier Method (ADMM) can efficiently find the optimal solution to the problem. Extensive experiments on real datasets show that our method outperforms other state-of-the-art methods.