Imperial College London
Alternate

STEFANOS ZAFEIRIOU, PhD

Faculty of Engineering, Department of Computing

Professor in Machine Learning & Computer Vision
 
 
 
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Contact

 

+44 (0)20 7594 8461s.zafeiriou Website CV

 
 
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Location

 

375Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Panagakis:2015:10.1109/TPAMI.2015.2497700,
author = {Panagakis, Y and Nicolaou, M and Zafeiriou, S and Pantic, M},
doi = {10.1109/TPAMI.2015.2497700},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
pages = {1665--1678},
title = {Robust correlated and individual component analysis},
url = {http://dx.doi.org/10.1109/TPAMI.2015.2497700},
volume = {38},
year = {2015}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Panagakis,Y
AU  - Nicolaou,M
AU  - Zafeiriou,S
AU  - Pantic,M
DO  - 10.1109/TPAMI.2015.2497700
EP  - 1678
PY  - 2015///
SN  - 0162-8828
SP  - 1665
TI  - Robust correlated and individual component analysis
T2  - IEEE Transactions on Pattern Analysis and Machine Intelligence
UR  - http://dx.doi.org/10.1109/TPAMI.2015.2497700
UR  - http://hdl.handle.net/10044/1/32929
VL  - 38
ER  -
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