Multilevel approximate robust principal component analysis

Conference paper


Hovhannisyan, V., Panagakis, Y., Zafeiriou, S. and Parpas, P. 2017. Multilevel approximate robust principal component analysis. 2017 IEEE International Conference on Computer Vision Workshop (ICCVW). Venice, Italy 22 - 29 Oct 2017 Institute of Electrical and Electronics Engineers (IEEE). pp. 536-544 https://doi.org/10.1109/ICCVW.2017.70
TypeConference paper
TitleMultilevel approximate robust principal component analysis
AuthorsHovhannisyan, V., Panagakis, Y., Zafeiriou, S. and Parpas, P.
Abstract

Robust principal component analysis (RPCA) is currently the method of choice for recovering a low-rank matrix from sparse corruptions that are of unknown value and support by decomposing the observation matrix into low-rank and sparse matrices. RPCA has many applications including background subtraction, learning of robust subspaces from visual data, etc. Nevertheless, the application of SVD in each iteration of optimisation methods renders the application of RPCA challenging in cases when data is large. In this paper, we propose the first, to the best of our knowledge, multilevel approach for solving convex and non-convex RPCA models. The basic idea is to construct lower dimensional models and perform SVD on them instead of the original high dimensional problem. We show that the proposed approach gives a good approximate solution to the original problem for both convex and non-convex formulations, while being many times faster than original RPCA methods in several real world datasets.

LanguageEnglish
Conference2017 IEEE International Conference on Computer Vision Workshop (ICCVW)
Page range536-544
ISSN2473-9944
ISBN
Hardcover9781538610343
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication dates
Print01 Oct 2017
Online23 Jan 2018
Publication process dates
Deposited07 Mar 2018
Accepted17 Jul 2017
Output statusPublished
Accepted author manuscript
Copyright Statement

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Digital Object Identifier (DOI)https://doi.org/10.1109/ICCVW.2017.70
Book title2017 IEEE International Conference on Computer Vision Workshop (ICCVW)
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