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Chunk Incremental LDA Computing on Data Streams

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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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.

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References

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Author information

Authors and Affiliations

  1. Knowledge Engineering & Discover Research Institute, Auckland University of Technology, Private Bag 92006, Auckland, 1020, New Zealand

    Shaoning Pang & Nikola Kasabov

  2. Graduate School of Science and Technology, Kobe University, Kobe, 657-8501, Japan

    Seiichi Ozawa

Authors
  1. Shaoning Pang
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  2. Seiichi Ozawa
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  3. Nikola Kasabov
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Pang, S., Ozawa, S., Kasabov, N. (2005). Chunk Incremental LDA Computing on Data Streams. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_9

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  • DOI: https://doi.org/10.1007/11427445_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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