Parametric Subspace Analysis for dimensionality reduction and classification

Vo, N; Vo, D; Challa, S; Moran, B

Parametric Subspace Analysis for dimensionality reduction and classification

Vo, N Vo, D Challa, S Moran, B

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM '09) Proceedings, 2009, pp. 363 - 366
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Vo, N	en_US
dc.contributor.author	Vo, D	en_US
dc.contributor.author	Challa, S	en_US
dc.contributor.author	Moran, B	en_US
dc.contributor.editor	IEEE	en_US
dc.date	2009-03-30	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM '09) Proceedings, 2009, pp. 363 - 366	en_US
dc.identifier.isbn	978-1-4244-2765-9	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10721
dc.description.abstract	Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are the two popular techniques in the context of dimensionality reduction and classification. By extracting discriminant features, LDA is optimal when the distributions of the features for each class are unimodal and separated by the scatter of means. On the other hand, PCA extract descriptive features which helps itself to outperform LDA in some classification tasks and less sensitive to different training data sets. The idea of Parametric Subspace Analysis (PSA) proposed in this paper is to include a parameter for regulating the combination of PCA and LDA. By combining descriptive (of PCA) and discriminant (of LDA) features, a better performance for dimensionality reduction and classification tasks is obtained with PSA and can be seen via our experimental results.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM '09) Proceedings	en_US
dc.relation.ispartof	IEEE Symposium on Computational Intelligence and Data Mining	en_US
dc.relation.isbasedon	10.1109/CIDM.2009.4938672	en_US
dc.rights	© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Parametric Subspace Analysis for dimensionality reduction and classification	en_US
dc.type	Conference Proceeding
utslib.location	USA	en_US
utslib.location.activity	Nashville, TN, USA	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Nashville, TN, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2009-03-30	en_US

Abstract:

Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) are the two popular techniques in the context of dimensionality reduction and classification. By extracting discriminant features, LDA is optimal when the distributions of the features for each class are unimodal and separated by the scatter of means. On the other hand, PCA extract descriptive features which helps itself to outperform LDA in some classification tasks and less sensitive to different training data sets. The idea of Parametric Subspace Analysis (PSA) proposed in this paper is to include a parameter for regulating the combination of PCA and LDA. By combining descriptive (of PCA) and discriminant (of LDA) features, a better performance for dimensionality reduction and classification tasks is obtained with PSA and can be seen via our experimental results.

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http://hdl.handle.net/10453/10721