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Just-in-time adaptive similarity component analysis in nonstationary environments

This article introduces a just-in-time adaptive nonparametric multiclass component analysis technique for application in nonstationary environments. This generative model enables adaptive similarity-based classifiers to classify time-labeled inquiry patterns with superior accuracy in low-dimensional...

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Main Authors:	Sojodishijani, Omid, Ramli, Abdul Rahman
Format:	Article
Language:	English
Published:	IOS Press 2014
Online Access:	http://psasir.upm.edu.my/id/eprint/34304/1/Just-in-time%20adaptive%20similarity%20component%20analysis%20in%20nonstationary%20environments.pdf http://psasir.upm.edu.my/id/eprint/34304/ http://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs853
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id	my.upm.eprints.34304
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spelling	my.upm.eprints.343042016-01-26T01:53:18Z http://psasir.upm.edu.my/id/eprint/34304/ Just-in-time adaptive similarity component analysis in nonstationary environments Sojodishijani, Omid Ramli, Abdul Rahman This article introduces a just-in-time adaptive nonparametric multiclass component analysis technique for application in nonstationary environments. This generative model enables adaptive similarity-based classifiers to classify time-labeled inquiry patterns with superior accuracy in low-dimensional feature space. While there are adaptive forms of feature extraction methods, which transform training patterns to low-dimensional space and/or improve classifier accuracy, they are vulnerable to nonparametric changes in data and must continuously update their parameters. In the proposed method, an optimal transformation matrix transforms time-labeled instances from the original space to a new feature space to maximize the probability of selecting the correct class label for incoming instances using similarity-based classifiers. To this end, for a given time-labeled instance, nonparametric intra-class and extra-class distributions are proposed. The proposed method is also furnished to a temporal detector to provide the most convenient time for the adaptation phase. Experimental results on real and synthesized datasets that include real and artificial changes demonstrate the performance of the proposed method in terms of accuracy and dimension reduction in dynamic environments. IOS Press 2014-07 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/34304/1/Just-in-time%20adaptive%20similarity%20component%20analysis%20in%20nonstationary%20environments.pdf Sojodishijani, Omid and Ramli, Abdul Rahman (2014) Just-in-time adaptive similarity component analysis in nonstationary environments. Journal of Intelligent and Fuzzy Systems, 26 (4). pp. 1745-1758. ISSN 1064-1246; ESSN: 1875-8967 http://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs853 10.3233/IFS-130853
institution	Universiti Putra Malaysia
building	UPM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Putra Malaysia
content_source	UPM Institutional Repository
url_provider	http://psasir.upm.edu.my/
language	English
description	This article introduces a just-in-time adaptive nonparametric multiclass component analysis technique for application in nonstationary environments. This generative model enables adaptive similarity-based classifiers to classify time-labeled inquiry patterns with superior accuracy in low-dimensional feature space. While there are adaptive forms of feature extraction methods, which transform training patterns to low-dimensional space and/or improve classifier accuracy, they are vulnerable to nonparametric changes in data and must continuously update their parameters. In the proposed method, an optimal transformation matrix transforms time-labeled instances from the original space to a new feature space to maximize the probability of selecting the correct class label for incoming instances using similarity-based classifiers. To this end, for a given time-labeled instance, nonparametric intra-class and extra-class distributions are proposed. The proposed method is also furnished to a temporal detector to provide the most convenient time for the adaptation phase. Experimental results on real and synthesized datasets that include real and artificial changes demonstrate the performance of the proposed method in terms of accuracy and dimension reduction in dynamic environments.
format	Article
author	Sojodishijani, Omid Ramli, Abdul Rahman
spellingShingle	Sojodishijani, Omid Ramli, Abdul Rahman Just-in-time adaptive similarity component analysis in nonstationary environments
author_facet	Sojodishijani, Omid Ramli, Abdul Rahman
author_sort	Sojodishijani, Omid
title	Just-in-time adaptive similarity component analysis in nonstationary environments
title_short	Just-in-time adaptive similarity component analysis in nonstationary environments
title_full	Just-in-time adaptive similarity component analysis in nonstationary environments
title_fullStr	Just-in-time adaptive similarity component analysis in nonstationary environments
title_full_unstemmed	Just-in-time adaptive similarity component analysis in nonstationary environments
title_sort	just-in-time adaptive similarity component analysis in nonstationary environments
publisher	IOS Press
publishDate	2014
url	http://psasir.upm.edu.my/id/eprint/34304/1/Just-in-time%20adaptive%20similarity%20component%20analysis%20in%20nonstationary%20environments.pdf http://psasir.upm.edu.my/id/eprint/34304/ http://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs853
_version_	1643831113697722368
score	13.211869

Just-in-time adaptive similarity component analysis in nonstationary environments

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