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

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)

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Official URL: https://link.springer.com/chapter/10.1007/978-3-64...

Abstract

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.

Item Type: Article
Uncontrolled Keywords: incremental learning, concept drift, online feature extraction, linear discriminant analysis, knowledge transfer, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Depositing User: Karen Kornalius
Date Deposited: 27 Sep 2017 07:06
Last Modified: 27 Sep 2017 07:06
URI: http://ir.unimas.my/id/eprint/17807

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