An Incremental Linear Discriminant Analysis for Data Streams Under Non-stationary Environments

Annie, Joseph and Young Min, Jang and Seiichi, Ozawa and Minho, Lee (2014) An Incremental Linear Discriminant Analysis for Data Streams Under Non-stationary Environments. Transactions of the Institute of Systems, Control and Information Engineers, 27 (4). pp. 133-140. ISSN 2185-811X

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In real life, data are not always generated under stationary environments. However, traditional learning systems have normally assumed that the property of data streams is stationary over time, and this sometimes leads to the degradation in the system performance when there are some hidden contexts changes (e.g. changes in class boundaries and temporal trends in time series). Such context changes are called concept drifts, and various methods to handle concept drifts have been developed in machine learning and data mining fields. However, most of them are aiming for building classifier models. Considering that class boundaries have changed over time under non-stationary environments, extracted features should also be adapted to concept drifts autonomously. In this paper, we propose an extension of incremental linear discriminant analysis (ILDA) as an online feature extraction method under non-stationary environments. The extended ILDA has the following two functions: concept-drift detection and knowledge transfer. The recognition performance of the extended ILDA is evaluated for three benchmark data sets. Experimental results demonstrate that the recognition performance in the extended ILDA is greatly improved by introducing the knowledge transfer after the concept-drift detection

Item Type: Article
Uncontrolled Keywords: concept drift, feature extraction , knowledge transfer, online learning.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Faculties, Institutes, Centres > Faculty of Engineering
Depositing User: Joseph
Date Deposited: 14 Sep 2022 08:38
Last Modified: 14 Sep 2022 08:38

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