NDLI: Adaptive incremental principal component analysis in nonstationary online learning environments

Content Provider	IEEE Xplore Digital Library
Author	Ozawa, S. Kawashima, Y. Shaoning Pang Kasabov, N.
Copyright Year	2009
Description	Author affiliation: Knowledge Engineering & Discover Research Institute, Auckland University of Technology, Private Bag 92006, 1142, New Zealand (Shaoning Pang; Kasabov, N.) \|\| Graduate School of Engineering, Kobe University, 657-8501, Japan (Ozawa, S.; Kawashima, Y.)
Abstract	In this paper, we propose a new Chunk IPCA algorithm in which an optimal threshold of accumulation ratio is adaptively selected such that the classification accuracy is maximized for a validation data set. In order to obtain a proper set of validation data, an online clustering method called Evolving Clustering Method (ECM) is introduced into Chunk IPCA. In the proposed Chunk IPCA called CIPCA-ECM, training data are first separated into the subsets of every class; then, ECM is applied to each subset to update the validation data set. In the experiments, the evaluation of the proposed Chunk IPCA algorithm is carried out using the four UCI data sets and the effectiveness of updating the threshold is discussed. The results suggest that the incremental learning of an eigenspace in the proposed CIPCA-ECM is stably carried out, and a compact and effective eigenspace is obtained over the entire learning stages. The recognition accuracy of CIPCA-ECM is almost equal to the best performance of CIPCA-FIX in which an optimal threshold is manually predetermined.
Starting Page	2394
Ending Page	2400
File Size	1466532
Page Count	7
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178997
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Electrochemical machining Feature extraction Clustering algorithms Clustering methods Training data Eigenvalues and eigenfunctions Neural networks Linear discriminant analysis Face recognition
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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