Non-iterative two-dimensional linear discriminant analysis

Kohei Inoue, Kiichi Urahama

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

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages540-543
Number of pages4
Volume2
DOIs
Publication statusPublished - 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: Aug 20 2006Aug 24 2006

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
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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  • Cite this

    Inoue, K., & Urahama, K. (2006). Non-iterative two-dimensional linear discriminant analysis. In Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006 (Vol. 2, pp. 540-543). [1699262] https://doi.org/10.1109/ICPR.2006.860