Improvement algorithm for approximate incremental learning

Tadahiro Oyama, H. Kipsang Choge, Stephen Karungaru, Satoru Tsuge, Yasue Mitsukura, Minoru Fukumi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents an improved algorithm of Incremental Simple-PCA. The Incremental Simple-PCA is a fast incremental learning algorithm based on Simple-PCA. This algorithm need not hold all training samples because it enables update of an eigenvector according to incremental samples. Moreover, this algorithm has an advantage that it can calculate the eigenvector at high-speed because matrix calculation is not needed. However, it had a problem in convergence performance of the eigenvector. Thus, in this paper, we try the improvement of this algorithm from the aspect of convergence performance. We performed computer simulations using UCI datasets to verify the effectiveness of the proposed algorithm. As a result, its availability was confirmed from the standpoint of recognition accuracy and convergence performance of the eigenvector compared with the Incremental Simple-PCA.

Original languageEnglish
Title of host publicationNeural Information Processing - 16th International Conference, ICONIP 2009, Proceedings
Pages520-529
Number of pages10
EditionPART 1
DOIs
Publication statusPublished - 2009 Dec 1
Externally publishedYes
Event16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, Thailand
Duration: 2009 Dec 12009 Dec 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5863 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Neural Information Processing, ICONIP 2009
CountryThailand
CityBangkok
Period09/12/109/12/5

Keywords

  • Cincremental learning Cdimensional reduction Cpattern recognition
  • PCA
  • Simple-PCA

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Oyama, T., Choge, H. K., Karungaru, S., Tsuge, S., Mitsukura, Y., & Fukumi, M. (2009). Improvement algorithm for approximate incremental learning. In Neural Information Processing - 16th International Conference, ICONIP 2009, Proceedings (PART 1 ed., pp. 520-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5863 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-10677-4_59