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Erschienen in: Soft Computing 16/2019

21.06.2018 | Methodologies and Application

Robust discriminant low-rank representation for subspace clustering

verfasst von: Xian Zhao, Gaoyun An, Yigang Cen, Hengyou Wang, Ruizhen Zhao

Erschienen in: Soft Computing | Ausgabe 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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Metadaten
Titel
Robust discriminant low-rank representation for subspace clustering
verfasst von
Xian Zhao
Gaoyun An
Yigang Cen
Hengyou Wang
Ruizhen Zhao
Publikationsdatum
21.06.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 16/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-3339-y

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