Side information in robust principal component analysis: algorithms and applications

Conference paper


Xue, N., Panagakis, Y. and Zafeiriou, S. 2017. Side information in robust principal component analysis: algorithms and applications. 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy 22 - 29 Oct 2017 IEEE. pp. 4327-4335 https://doi.org/10.1109/ICCV.2017.463
TypeConference paper
TitleSide information in robust principal component analysis: algorithms and applications
AuthorsXue, N., Panagakis, Y. and Zafeiriou, S.
Abstract

Robust Principal Component Analysis (RPCA) aims at recovering a low-rank subspace from grossly corrupted high-dimensional (often visual) data and is a cornerstone in many machine learning and computer vision applications. Even though RPCA has been shown to be very successful in solving many rank minimisation problems, there are still cases where degenerate or suboptimal solutions are obtained. This is likely to be remedied by taking into account of domain-dependent prior knowledge. In this paper, we propose two models for the RPCA problem with the aid of side information on the low-rank structure of the data. The versatility of the proposed methods is demonstrated by applying them to four applications, namely background subtraction, facial image denoising, face and facial expression recognition. Experimental results on synthetic and five real world datasets indicate the robustness and effectiveness of the proposed methods on these application domains, largely outperforming six previous approaches.

LanguageEnglish
Conference2017 IEEE International Conference on Computer Vision (ICCV)
Page range4327-4335
Proceedings Title2017 IEEE International Conference on Computer Vision (ICCV)
ISSN
Electronic2380-7504
ISBN
Electronic9781538610329
Hardcover9781538610336
PublisherIEEE
Publication dates
Print01 Oct 2017
Online25 Dec 2017
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/ICCV.2017.463
Book title2017 IEEE International Conference on Computer Vision (ICCV)
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