Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview

• Published in: IEEE transactions on cybernetics Vol. 52; no. 9; pp. 8976 - 8986
• Main Authors: Kang, Zhao, Lin, Zhiping, Zhu, Xiaofeng, Xu, Wenbo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anchor graph; Bipartite graph; Clustering; Clustering algorithms; Clustering methods; connectivity constraint; Data points; Graph theory; Laplace equations; large-scale data; Machine learning; multiview learning; out of sample; Periodic structures; subspace clustering; Subspace methods; Training; Videos

Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: they encounter the expensive time overhead, they fail to explore the explicit clusters, and cannot generalize to unseen data points. In this work, we propose a scalable g...