Chunk incremental LDA computing on data streams

Authors:
Shaoning Pang;Seiichi Ozawa;Nikola Kasabov
Affiliations:
Knowledge Engineering & Discover Research Institute, Auckland University of Technology, Auckland, New Zealand;Graduate School of Science and Technology, Kobe University, Kobe, Japan;Knowledge Engineering & Discover Research Institute, Auckland University of Technology, Auckland, New Zealand
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

This paper presents a constructive method for deriving an updated discriminant eigenspace for classification, when bursts of new classes of data is being added to an initial discriminant eigenspace in the form of random chunks. The proposed Chunk incremental linear discriminant analysis (I-LDA) can effectively evolve a discriminant eigenspace over a fast and large data stream, and extract features with superior discriminability in classification, when compared with other methods.