Improvement algorithm for approximate incremental learning

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

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 16th International Conference, ICONIP 2009, Proceedings
ページ520-529
ページ数10
PART 1
DOI
出版ステータスPublished - 2009 12 1
外部発表はい
イベント16th International Conference on Neural Information Processing, ICONIP 2009 - Bangkok, Thailand
継続期間: 2009 12 12009 12 5

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 1
5863 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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