Extension of Incremental Linear Discriminant Analysis to Online Feature Extraction under Nonstationary Environments

In this paper, a new approach to an online feature extraction under nonstationary environments is proposed by extending Incremental Linear Discriminant Analysis (ILDA). The extended ILDA not only detect so-called “concept drifts” but also transfer the knowledge on discriminant feature spaces of the...

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Main Authors: Joseph, A., Jang, Young-Min, Ozawa, Seiichi, Lee, Minho
Format: Article
Language:English
Published: Springer-Verlag Berlin Heidelberg 2012
Subjects:
Online Access:http://ir.unimas.my/id/eprint/17807/
http://ir.unimas.my/id/eprint/17807/1/Extension%20of%20Incremental%20Linear%20Discriminant%20%28abstract%29.pdf
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author Joseph, A.
Jang, Young-Min
Ozawa, Seiichi
Lee, Minho
author_facet Joseph, A.
Jang, Young-Min
Ozawa, Seiichi
Lee, Minho
author_sort Joseph, A.
building UNIMAS Institutional Repository
collection Online Access
description In this paper, a new approach to an online feature extraction under nonstationary environments is proposed by extending Incremental Linear Discriminant Analysis (ILDA). The extended ILDA not only detect so-called “concept drifts” but also transfer the knowledge on discriminant feature spaces of the past concepts to construct good feature spaces. The performance of the extended ILDA is evaluated for the benchmark datasets including sudden changes and reoccurrence in concepts.
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institution Universiti Malaysia Sarawak
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language English
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spelling unimas-178072017-09-27T07:06:54Z http://ir.unimas.my/id/eprint/17807/ Extension of Incremental Linear Discriminant Analysis to Online Feature Extraction under Nonstationary Environments Joseph, A. Jang, Young-Min Ozawa, Seiichi Lee, Minho T Technology (General) In this paper, a new approach to an online feature extraction under nonstationary environments is proposed by extending Incremental Linear Discriminant Analysis (ILDA). The extended ILDA not only detect so-called “concept drifts” but also transfer the knowledge on discriminant feature spaces of the past concepts to construct good feature spaces. The performance of the extended ILDA is evaluated for the benchmark datasets including sudden changes and reoccurrence in concepts. Springer-Verlag Berlin Heidelberg 2012 Article PeerReviewed text en http://ir.unimas.my/id/eprint/17807/1/Extension%20of%20Incremental%20Linear%20Discriminant%20%28abstract%29.pdf Joseph, A. and Jang, Young-Min and Ozawa, Seiichi and Lee, Minho (2012) Extension of Incremental Linear Discriminant Analysis to Online Feature Extraction under Nonstationary Environments. International Conference on Neural Information Processing. pp. 640-647. ISSN 978-3-642-34487-9 (ISBN) https://link.springer.com/chapter/10.1007/978-3-642-34481-7_78
spellingShingle T Technology (General)
Joseph, A.
Jang, Young-Min
Ozawa, Seiichi
Lee, Minho
Extension of Incremental Linear Discriminant Analysis to Online Feature Extraction under Nonstationary Environments
title Extension of Incremental Linear Discriminant Analysis to Online Feature Extraction under Nonstationary Environments
title_full Extension of Incremental Linear Discriminant Analysis to Online Feature Extraction under Nonstationary Environments
title_fullStr Extension of Incremental Linear Discriminant Analysis to Online Feature Extraction under Nonstationary Environments
title_full_unstemmed Extension of Incremental Linear Discriminant Analysis to Online Feature Extraction under Nonstationary Environments
title_short Extension of Incremental Linear Discriminant Analysis to Online Feature Extraction under Nonstationary Environments
title_sort extension of incremental linear discriminant analysis to online feature extraction under nonstationary environments
topic T Technology (General)
url http://ir.unimas.my/id/eprint/17807/
http://ir.unimas.my/id/eprint/17807/
http://ir.unimas.my/id/eprint/17807/1/Extension%20of%20Incremental%20Linear%20Discriminant%20%28abstract%29.pdf