TY - JOUR AU - Gao, Quanxue AU - Xia, Wei AU - Wan, Zhizhen AU - Xie, Deyan AU - Zhang, Pu PY - 2020/04/03 Y2 - 2024/03/29 TI - Tensor-SVD Based Graph Learning for Multi-View Subspace Clustering JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 04 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v34i04.5807 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5807 SP - 3930-3937 AB - <p>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 multi-view data. The major reason is that all the singular values have the same contribution in tensor-nuclear norm based on t-SVD, which does not make sense in the existence of noise and illumination change. To improve the robustness and clustering performance, we study the weighted tensor-nuclear norm based on t-SVD and develop an efficient algorithm to optimize the weighted tensor-nuclear norm minimization (WTNNM) problem. We further apply the WTNNM algorithm to multi-view subspace clustering by exploiting the high order correlations embedded in different views. Extensive experimental results reveal that our WTNNM method is superior to several state-of-the-art multi-view subspace clustering methods in terms of performance.</p> ER -