Feature Selection Embedded Subspace Clustering

Chong Peng; Zhao Kang; Ming Yang; Qiang Cheng

doi:10.1109/LSP.2016.2573159

Feature Selection Embedded Subspace Clustering

Chong Peng
, Zhao Kang
, Ming Yang
, Qiang Cheng

Internal Medicine

Producción científica: Article › revisión exhaustiva

51 Citas (Scopus)

Resumen

We propose a new subspace clustering method that integrates feature selection into subspace clustering. Rather than using all features to construct a low-rank representation of the data, we find such a representation using only relevant features, which helps in revealing more accurate data relationships. Two variants are proposed by using both convex and nonconvex rank approximations. Extensive experimental results confirm the effectiveness of the proposed method and models.

Idioma original	English
Número de artículo	7479529
Páginas (desde-hasta)	1018-1022
Número de páginas	5
Publicación	IEEE Signal Processing Letters
Volumen	23
N.º	7
DOI	https://doi.org/10.1109/LSP.2016.2573159
Estado	Published - jul 2016

Nota bibliográfica

Publisher Copyright:
© 1994-2012 IEEE.

Financiación

Manuscript received April 20, 2016; revisedMay 16, 2016; accepted May 16, 2016. Date of publication May 26, 2016; date of current version June 23, 2016. This work was supported by NSF under Grant IIS-1218712. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Marco Duarte.

Financiadores	Número del financiador
National Science Foundation (NSF)	1218712, IIS-1218712

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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Feature Selection Embedded Subspace Clustering

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ASJC Scopus subject areas

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