Extension of incremental linear discriminant analysis to online feature extraction under nonstationary environments

Authors:
Annie anak Joseph;Young-Min Jang;Seiichi Ozawa;Minho Lee
Affiliations:
Graduate School of Engineering, Kobe University, Nada-ku, Kobe, Japan;Electrical and Electronic Engineering, Kyungpook National University, Buk-gu, Taegu, Korea;Graduate School of Engineering, Kobe University, Nada-ku, Kobe, Japan;Electrical and Electronic Engineering, Kyungpook National University, Buk-gu, Taegu, Korea
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Year:
2012

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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.