Side information in robust principal component analysis: algorithms and applications

Xue, Niannan and Panagakis, Yannis and Zafeiriou, Stefanos (2017) Side information in robust principal component analysis: algorithms and applications. In: 2017 IEEE International Conference on Computer Vision (ICCV), 22-29 Oct 2017, Venice, Italy.

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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.

Item Type: Conference or Workshop Item (Paper)
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 23791
Notes on copyright: © 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
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Depositing User: Yannis Panagakis
Date Deposited: 07 Mar 2018 16:27
Last Modified: 07 Dec 2018 08:20
URI: http://eprints.mdx.ac.uk/id/eprint/23791

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