Imperial College London

STEFANOS ZAFEIRIOU, PhD

Faculty of EngineeringDepartment 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{Nicolaou:2014:10.1007/978-3-662-44851-9_30,
author = {Nicolaou, MA and Zafeiriou, S and Pantic, M},
doi = {10.1007/978-3-662-44851-9_30},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {469--484},
title = {A unified framework for probabilistic component analysis},
url = {http://dx.doi.org/10.1007/978-3-662-44851-9_30},
volume = {8725 LNAI},
year = {2014}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - We present a unifying framework which reduces the construction of probabilistic component analysis techniques to a mere selection of the latent neighbourhood, thus providing an elegant and principled framework for creating novel component analysis models as well as constructing probabilistic equivalents of deterministic component analysis methods. Under our framework, we unify many very popular and well-studied component analysis algorithms, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Locality Preserving Projections (LPP) and Slow Feature Analysis (SFA), some of which have no probabilistic equivalents in literature thus far. We firstly define the Markov Random Fields (MRFs) which encapsulate the latent connectivity of the aforementioned component analysis techniques; subsequently, we show that the projection directions produced by all PCA, LDA, LPP and SFA are also produced by the Maximum Likelihood (ML) solution of a single joint probability density function, composed by selecting one of the defined MRF priors while utilising a simple observation model. Furthermore, we propose novel Expectation Maximization (EM) algorithms, exploiting the proposed joint PDF, while we generalize the proposed methodologies to arbitrary connectivities via parametrizable MRF products. Theoretical analysis and experiments on both simulated and real world data show the usefulness of the proposed framework, by deriving methods which well outperform state-of-the-art equivalents. © 2014 Springer-Verlag.
AU - Nicolaou,MA
AU - Zafeiriou,S
AU - Pantic,M
DO - 10.1007/978-3-662-44851-9_30
EP - 484
PY - 2014///
SN - 0302-9743
SP - 469
TI - A unified framework for probabilistic component analysis
T2 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
UR - http://dx.doi.org/10.1007/978-3-662-44851-9_30
VL - 8725 LNAI
ER -