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Robust Correlated and Individual Component Analysis

Panagakis, Yannis; Nicolaou, Mihalis; 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. [Article]

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Abstract or Description

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

Identification Number (DOI):

https://doi.org/10.1109/TPAMI.2015.2497700

Additional Information:

pre-print (https://www.computer.org/csdl/trans/tp/preprint/07317798-abs.html)

This work has been funded by the European Community Horizon 2020 [H2020/2014-2020] under grant agreement no. 645094 (SEWA). Yannis Panagakis was also partially funded by the ERC under the FP7 Marie Curie Intra-European Fellowship. The work of Mihalis Nicolaou was also funded in part by the European Community 7th Framework Programme [FP7/2007-2013] under grant agreement no. 611153 (TERESA). Stefanos Zafeiriou was partially supported by the EPSRC project EP/J017787/1 (4D-FAB).

Keywords:

Multi-modal analysis, canonical correlation analysis, individual components, time warping, low-rank, sparsity

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
1 August 2016Published
4 November 2015Published Online

Item ID:

17321

Date Deposited:

22 Mar 2016 08:39

Last Modified:

11 Jul 2018 06:33

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/17321

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