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 |
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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 | |
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 |
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Volume | 24-28-October-2016 |
Conference
Conference | 25th ACM International Conference on Information and Knowledge Management, CIKM 2016 |
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Country/Territory | United States |
City | Indianapolis |
Period | 10/24/16 → 10/28/16 |
Bibliographical note
Publisher Copyright:© 2016 Copyright held by the owner/author(s).
Keywords
- Fixed rank
- Low-rank recovery
- RPCA
- Scabality
ASJC Scopus subject areas
- General Business, Management and Accounting
- General Decision Sciences