Low-rank approximation-based tensor decomposition model for subspace clustering

Low-rank approximation-based tensor decomposition model for subspace clustering

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.

Related content

This is a required field
Please enter a valid email address