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Authors: Ahmed Otoom ; Oscar Perez Concha and Massimo Piccardi

Affiliation: University of Technology, Sydney (UTS), Australia

Keyword(s): Dimensionality reduction, Linear transformation, Random projections, Mixture models, Object classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: High dimensional spaces pose a serious challenge to the learning process. It is a combination of limited number of samples and high dimensions that positions many problems under the “curse of dimensionality”, which restricts severely the practical application of density estimation. Many techniques have been proposed in the past to discover embedded, locally-linear manifolds of lower dimensionality, including the mixture of Principal Component Analyzers, the mixture of Probabilistic Principal Component Analyzers and the mixture of Factor Analyzers. In this paper, we present a mixture model for reducing dimensionality based on a linear transformation which is not restricted to be orthogonal. Two methods are proposed for the learning of all the transformations and mixture parameters: the first method is based on an iterative maximum-likelihood approach and the second is based on random transformations and fixed (non iterative) probability functions. For experimental validation, we have used the proposed model for maximum-likelihood classification of five “hard” data sets including data sets from the UCI repository and the authors’ own. Moreover, we compared the classification performance of the proposed method with that of other popular classifiers including the mixture of Probabilistic Principal Component Analyzers and the Gaussian mixture model. In all cases but one, the accuracy achieved by the proposed method proved the highest, with increases with respect to the runner-up ranging from 0.2% to 5.2%. (More)

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Paper citation in several formats:
Otoom, A.; Perez Concha, O. and Piccardi, M. (2010). MIXTURES OF GAUSSIAN DISTRIBUTIONS UNDER LINEAR DIMENSIONALITY REDUCTION. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2010) - Volume 2: VISAPP; ISBN 978-989-674-029-0; ISSN 2184-4321, SciTePress, pages 511-518. DOI: 10.5220/0002844005110518

@conference{visapp10,
author={Ahmed Otoom. and Oscar {Perez Concha}. and Massimo Piccardi.},
title={MIXTURES OF GAUSSIAN DISTRIBUTIONS UNDER LINEAR DIMENSIONALITY REDUCTION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2010) - Volume 2: VISAPP},
year={2010},
pages={511-518},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002844005110518},
isbn={978-989-674-029-0},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2010) - Volume 2: VISAPP
TI - MIXTURES OF GAUSSIAN DISTRIBUTIONS UNDER LINEAR DIMENSIONALITY REDUCTION
SN - 978-989-674-029-0
IS - 2184-4321
AU - Otoom, A.
AU - Perez Concha, O.
AU - Piccardi, M.
PY - 2010
SP - 511
EP - 518
DO - 10.5220/0002844005110518
PB - SciTePress