Robust correlated and individual component analysis

Panagakis, Yannis and Nicolaou, Mihalis A. and Zafeiriou, Stefanos and Pantic, Maja (2016) Robust correlated and individual component analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38 (8). pp. 1665-1678. ISSN 0162-8828

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Abstract

Recovering correlated and individual components of two, possibly temporally misaligned, sets of data is a fundamental task in disciplines such as image, vision, and behavior computing, with application to problems such as multi-modal fusion (via correlated components), predictive analysis, and clustering (via the individual ones). Here, we study the extraction of correlated and individual components under real-world conditions, namely i) the presence of gross non-Gaussian noise and ii) temporally misaligned data. In this light, we propose a method for the Robust Correlated and Individual Component Analysis (RCICA) of two sets of data in the presence of gross, sparse errors. We furthermore extend RCICA in order to handle temporal incongruities arising in the data. To this end, two suitable optimization problems are solved. The generality of the proposed methods is demonstrated by applying them onto 4 applications, namely i) heterogeneous face recognition, ii) multi-modal feature fusion for human behavior analysis (i.e., audio-visual prediction of interest and conflict), iii) face clustering, and iv) the temporal alignment of facial expressions. Experimental results on 2 synthetic and 7 real world datasets indicate the robustness and effectiveness of the proposed methods on these application domains, outperforming other state-of-the-art methods in the field.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 23764
Notes on copyright: © 2015 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: 06 Mar 2018 17:10
Last Modified: 07 Dec 2018 08:25
URI: http://eprints.mdx.ac.uk/id/eprint/23764

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