Open AccessLow‐rank approximation‐based tensor decomposition model for subspace clustering
Author(s) -
Su Yuting,
Bai Xu,
Jian Pu,
Jing Peiguang,
Zhang Jing
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.8240
Subject(s) - tensor (intrinsic definition) , subspace topology , projection (relational algebra) , rank (graph theory) , cluster analysis , representation (politics) , mathematics , algorithm , noise (video) , pattern recognition (psychology) , computer science , artificial intelligence , combinatorics , pure mathematics , image (mathematics) , politics , political science , law
To better explore the underlying intrinsic structure of tensorial data, in this Letter, the authors propose a low‐rank approximation‐based tensor decomposition (LRATD) algorithm for subspace clustering. LRATD aims to seek a low‐dimensional intrinsic core tensor representation by projecting the original tensor into a subspace spanned by projection matrices. Different from traditional approaches that impose additional constraints on basis matrices to further eliminate the influence of data noise or corruption, they directly add a low‐rank regulariser on the core tensor to encourage more robust feature representation. Noticeably, they develop an accelerated proximal gradient algorithm to solve the problem of LRATD. Experimental results demonstrate the excellent performance compared with state‐of‐the‐art methods.