Paper Info
Title
Adaptive multi-view subspace clustering for high-dimensional data
Abstract
•Perform adaptive feature learning and multi-view clustering jointly.•Impose a special selection matrix to select the optimal feature subset and to avoid any eigenvalue decomposition.•Propose an efficient algorithm to solve the non-smooth objective function.
Year
DOI
Venue
2020
10.1016/j.patrec.2019.01.016
Pattern Recognition Letters
Keywords
Field
DocType
Subspace clustering,Multi-view clustering,Adaptive learning,Feature selection
Clustering high-dimensional data,Feature vector,Pattern recognition,Subspace topology,Projection (linear algebra),Compact space,Artificial intelligence,Eigendecomposition of a matrix,Cluster analysis,Mathematics,Feature learning
Journal
Volume
ISSN
Citations 
130
0167-8655
2
PageRank 
References 
Authors
0.35
23
4
Name
Order
Citations
PageRank
Fei Yan1289.01
Xiaodong Wang25711.61
Zhiqiang Zeng313916.35
Chaoqun Hong432413.19