loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Ahmed Otoom ; Oscar Perez Concha and Massimo Piccardi

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

ISBN: 978-989-674-029-0

ISSN: 2184-4321

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)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.175.121.230

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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

TY - CONF

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.