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

Research output: Contribution to journal › Article › peer-review

46 Scopus citations

Abstract

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.

Original language	English
Article number	7479529
Pages (from-to)	1018-1022
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	23
Issue number	7
DOIs	https://doi.org/10.1109/LSP.2016.2573159
State	Published - Jul 2016

Bibliographical note

Publisher Copyright:
© 1994-2012 IEEE.

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/LSP.2016.2573159

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abstract = "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.",

author = "Chong Peng and Zhao Kang and Ming Yang and Qiang Cheng",

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AU - Cheng, Qiang

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AB - 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.

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Feature Selection Embedded Subspace Clustering

Abstract

Bibliographical note

ASJC Scopus subject areas

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