RAP: Scalable RPCA for low-rank matrix recovery

Chong Peng, Zhao Kang, Ming Yang, Qiang Cheng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


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 languageEnglish
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
Number of pages6
ISBN (Electronic)9781450340731
StatePublished - Oct 24 2016
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: Oct 24 2016Oct 28 2016

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/TerritoryUnited States

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).


  • Fixed rank
  • Low-rank recovery
  • RPCA
  • Scabality

ASJC Scopus subject areas

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


Dive into the research topics of 'RAP: Scalable RPCA for low-rank matrix recovery'. Together they form a unique fingerprint.

Cite this