RAP: Scalable RPCA for low-rank matrix recovery

Chong Peng; Zhao Kang; Ming Yang; Qiang Cheng

doi:10.1145/2983323.2983651

RAP: Scalable RPCA for low-rank matrix recovery

Chong Peng, Zhao Kang, Ming Yang, Qiang Cheng

Internal Medicine

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

3 Scopus citations

Abstract

Recovering low-rank matrices is a problem common in many applications of data mining and machine learning, such as matrix completion and image denoising. Robust Principal Component Analysis (RPCA) has emerged for handling such kinds of problems; however, the existing RPCA approaches are usually computationally expensive, due to the fact that they need to obtain the singular value decomposition (SVD) of large matrices. In this paper, we propose a novel RPCA approach that eliminates the need for SVD of large matrices. Scalable algorithms are designed for several variants of our approach, which are crucial for real world applications on large scale data. Extensive experimental results confirm the effectiveness of our approach both quantitatively and visually.

Original language	English
Title of host publication	CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
Pages	2113-2118
Number of pages	6
ISBN (Electronic)	9781450340731
DOIs	https://doi.org/10.1145/2983323.2983651
State	Published - Oct 24 2016
Event	25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States Duration: Oct 24 2016 → Oct 28 2016

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings
Volume	24-28-October-2016

Conference

Conference	25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/Territory	United States
City	Indianapolis
Period	10/24/16 → 10/28/16

Bibliographical note

Funding Information:
This work is supported by National Science Foundation under grant IIS-1218712, National Natural Science Foundation of China, under grant 11241005, and Shanxi Scholarship Council of China 2015-093, Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province.

Publisher Copyright:
© 2016 Copyright held by the owner/author(s).

Keywords

Fixed rank
Low-rank recovery
RPCA
Scabality

ASJC Scopus subject areas

Decision Sciences (all)
Business, Management and Accounting (all)

Access to Document

10.1145/2983323.2983651

Cite this

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title = "RAP: Scalable RPCA for low-rank matrix recovery",

abstract = "Recovering low-rank matrices is a problem common in many applications of data mining and machine learning, such as matrix completion and image denoising. Robust Principal Component Analysis (RPCA) has emerged for handling such kinds of problems; however, the existing RPCA approaches are usually computationally expensive, due to the fact that they need to obtain the singular value decomposition (SVD) of large matrices. In this paper, we propose a novel RPCA approach that eliminates the need for SVD of large matrices. Scalable algorithms are designed for several variants of our approach, which are crucial for real world applications on large scale data. Extensive experimental results confirm the effectiveness of our approach both quantitatively and visually.",

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doi = "10.1145/2983323.2983651",

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Peng, C, Kang, Z, Yang, M & Cheng, Q 2016, RAP: Scalable RPCA for low-rank matrix recovery. in CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, vol. 24-28-October-2016, pp. 2113-2118, 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, United States, 10/24/16. https://doi.org/10.1145/2983323.2983651

RAP : Scalable RPCA for low-rank matrix recovery. / Peng, Chong; Kang, Zhao; Yang, Ming et al.

CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management. 2016. p. 2113-2118 (International Conference on Information and Knowledge Management, Proceedings; Vol. 24-28-October-2016).

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

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AU - Yang, Ming

AU - Cheng, Qiang

N1 - Funding Information: This work is supported by National Science Foundation under grant IIS-1218712, National Natural Science Foundation of China, under grant 11241005, and Shanxi Scholarship Council of China 2015-093, Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province. Publisher Copyright: © 2016 Copyright held by the owner/author(s).

PY - 2016/10/24

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N2 - Recovering low-rank matrices is a problem common in many applications of data mining and machine learning, such as matrix completion and image denoising. Robust Principal Component Analysis (RPCA) has emerged for handling such kinds of problems; however, the existing RPCA approaches are usually computationally expensive, due to the fact that they need to obtain the singular value decomposition (SVD) of large matrices. In this paper, we propose a novel RPCA approach that eliminates the need for SVD of large matrices. Scalable algorithms are designed for several variants of our approach, which are crucial for real world applications on large scale data. Extensive experimental results confirm the effectiveness of our approach both quantitatively and visually.

AB - Recovering low-rank matrices is a problem common in many applications of data mining and machine learning, such as matrix completion and image denoising. Robust Principal Component Analysis (RPCA) has emerged for handling such kinds of problems; however, the existing RPCA approaches are usually computationally expensive, due to the fact that they need to obtain the singular value decomposition (SVD) of large matrices. In this paper, we propose a novel RPCA approach that eliminates the need for SVD of large matrices. Scalable algorithms are designed for several variants of our approach, which are crucial for real world applications on large scale data. Extensive experimental results confirm the effectiveness of our approach both quantitatively and visually.

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RAP: Scalable RPCA for low-rank matrix recovery

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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