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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Subspace Clustering by Capped \(l_1\) Norm

verfasst von : Quanmao Lu, Xuelong Li, Yongsheng Dong, Dacheng Tao

Erschienen in: Pattern Recognition

Verlag: Springer Singapore

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Abstract

Subspace clustering, as an important clustering problem, has drawn much attention in recent years. State-of-the-art methods generally try to design an efficient model to regularize the coefficient matrix while ignore the influence of the noise model on subspace clustering. However, the real data are always contaminated by the noise and the corresponding subspace structures are likely to be corrupted. In order to solve this problem, we propose a novel subspace clustering algorithm by employing capped \(l_1\) norm to deal with the noise. Consequently, the noise term with large error can be penalized by the proposed method. So it is more robust to the noise. Furthermore, the grouping effect of our method is theoretically proved, which means highly correlated points can be grouped together. Finally, the experimental results on two real databases show that our method outperforms state-of-the-art methods.

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Vorheriges Kapitel Analysis of Convergence Properties for Gath-Geva Clustering Using Jacobian Matrix
Nächstes Kapitel Clique-Based Locally Consistent Latent Space Clustering for Community Detection
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Metadaten
Titel
Subspace Clustering by Capped Norm
verfasst von
Quanmao Lu
Xuelong Li
Yongsheng Dong
Dacheng Tao
Copyright-Jahr
2016
Verlag
Springer Singapore
Buch
Pattern Recognition

Print ISBN: 978-981-10-3001-7

Electronic ISBN: 978-981-10-3002-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-981-10-3002-4_54

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