Multi-view subspace clustering

Hongchang Gao; Feiping Nie; Xuelong Li; Heng Huang

doi:10.1109/ICCV.2015.482

Multi-view subspace clustering

Hongchang Gao, Feiping Nie, Xuelong Li, Heng Huang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

561 Scopus citations

Abstract

For many computer vision applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. The proposed method performs clustering on the subspace representation of each view simultaneously. Meanwhile, we propose to use a common cluster structure to guarantee the consistence among different views. In addition, an efficient algorithm is proposed to solve the problem. Experiments on four benchmark data sets have been performed to validate our proposed method. The promising results demonstrate the effectiveness of our method.

Original language	English
Title of host publication	2015 International Conference on Computer Vision, ICCV 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4238-4246
Number of pages	9
ISBN (Electronic)	9781467383912
DOIs	https://doi.org/10.1109/ICCV.2015.482
State	Published - 17 Feb 2015
Externally published	Yes
Event	15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile Duration: 11 Dec 2015 → 18 Dec 2015

Publication series

Name	Proceedings of the IEEE International Conference on Computer Vision
Volume	2015 International Conference on Computer Vision, ICCV 2015
ISSN (Print)	1550-5499

Conference

Conference	15th IEEE International Conference on Computer Vision, ICCV 2015
Country/Territory	Chile
City	Santiago
Period	11/12/15 → 18/12/15

Access to Document

10.1109/ICCV.2015.482

Cite this

Gao, H., Nie, F., Li, X., & Huang, H. (2015). Multi-view subspace clustering. In 2015 International Conference on Computer Vision, ICCV 2015 (pp. 4238-4246). Article 7410839 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2015 International Conference on Computer Vision, ICCV 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2015.482

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title = "Multi-view subspace clustering",

abstract = "For many computer vision applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. The proposed method performs clustering on the subspace representation of each view simultaneously. Meanwhile, we propose to use a common cluster structure to guarantee the consistence among different views. In addition, an efficient algorithm is proposed to solve the problem. Experiments on four benchmark data sets have been performed to validate our proposed method. The promising results demonstrate the effectiveness of our method.",

author = "Hongchang Gao and Feiping Nie and Xuelong Li and Heng Huang",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 15th IEEE International Conference on Computer Vision, ICCV 2015 ; Conference date: 11-12-2015 Through 18-12-2015",

year = "2015",

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doi = "10.1109/ICCV.2015.482",

language = "英语",

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Gao, H, Nie, F, Li, X & Huang, H 2015, Multi-view subspace clustering. in 2015 International Conference on Computer Vision, ICCV 2015., 7410839, Proceedings of the IEEE International Conference on Computer Vision, vol. 2015 International Conference on Computer Vision, ICCV 2015, Institute of Electrical and Electronics Engineers Inc., pp. 4238-4246, 15th IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, 11/12/15. https://doi.org/10.1109/ICCV.2015.482

Multi-view subspace clustering. / Gao, Hongchang; Nie, Feiping; Li, Xuelong et al.
2015 International Conference on Computer Vision, ICCV 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 4238-4246 7410839 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2015 International Conference on Computer Vision, ICCV 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Huang, Heng

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N2 - For many computer vision applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. The proposed method performs clustering on the subspace representation of each view simultaneously. Meanwhile, we propose to use a common cluster structure to guarantee the consistence among different views. In addition, an efficient algorithm is proposed to solve the problem. Experiments on four benchmark data sets have been performed to validate our proposed method. The promising results demonstrate the effectiveness of our method.

AB - For many computer vision applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is to find such underlying subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. The proposed method performs clustering on the subspace representation of each view simultaneously. Meanwhile, we propose to use a common cluster structure to guarantee the consistence among different views. In addition, an efficient algorithm is proposed to solve the problem. Experiments on four benchmark data sets have been performed to validate our proposed method. The promising results demonstrate the effectiveness of our method.

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T2 - 15th IEEE International Conference on Computer Vision, ICCV 2015

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Multi-view subspace clustering

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