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Soft Computing

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21-06-2018 | Methodologies and Application

Robust discriminant low-rank representation for subspace clustering

Authors: Xian Zhao, Gaoyun An, Yigang Cen, Hengyou Wang, Ruizhen Zhao

Published in: Soft Computing | Issue 16/2019

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Abstract

For the low-rank representation-based subspace clustering, the affinity matrix is block diagonal. In this paper, a novel robust discriminant low-rank representation (RDLRR) algorithm is proposed to enhance the block diagonal property to explore the multiple subspace structures of samples. In order to cluster samples into their corresponding subspace and remove outliers, the proposed RDLRR considers both the within-class and the between-class distance during seeking the lowest-rank representation of samples. RDLRR could well indicate the global structure of samples, when the labeling is available. We conduct experiments on several datasets, including the Extended Yale B, AR and Hopkins 155, to show that our approach outperforms all the other state-of-the-art approaches.

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Title: Robust discriminant low-rank representation for subspace clustering
Authors: Xian Zhao
Gaoyun An
Yigang Cen
Hengyou Wang
Ruizhen Zhao
Publication date: 21-06-2018
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 16/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3339-y

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