Non-iterative two-dimensional linear discriminant analysis

Kohei Inoue, Kiichi Urahama

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

15 被引用数 (Scopus)

抄録

Linear discriminant analysis (LDA) is a well-known scheme for feature extraction and dimensionality reduction of labeled data in a vector space. Recently, LDA has been extended to two-dimensional LDA (2DLDA), which is an iterative algorithm for data in matrix representation. In this paper, we propose non-iterative algorithms for 2DLDA. Experimental results show that the non-iterative algorithms achieve competitive recognition rates with the iterative 2DLDA, while they are computationally more efficient than the iterative 2DLDA.

本文言語英語
ホスト出版物のタイトルProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
ページ540-543
ページ数4
2
DOI
出版ステータス出版済み - 2006
イベント18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, 中国
継続期間: 8 20 20068 24 2006

その他

その他18th International Conference on Pattern Recognition, ICPR 2006
Country中国
CityHong Kong
Period8/20/068/24/06

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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