Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering

• DOI: 10.1609/aaai.v34i04.5807
• Publication Date: 2020-06-29T21:23:59Z
• Authors: Quanxue Gao; Wei Xia; Zhizhen Wan; Deyan Xie; Pu Zhang
• Keywords: Singular value; Matrix norm; Robustness

Low-rank representation based on tensor-Singular Value Decomposition (t-SVD) has achieved impressive results for multi-view subspace clustering, but it does not well deal with noise and illumination changes embedded in data. The major reason is that all the singular values have same contribution tensor-nuclear norm t-SVD, which make sense existence of change. To improve robustness clustering performance, we study weighted t-SVD develop an efficient algorithm to optimize minimization (WTNNM) problem. We further apply WTNNM by exploiting high order correlations different views. Extensive experimental reveal our method superior several state-of-the-art methods terms performance.