A Simple feature generation method based on fisher linear discriminant analysis

Minoru Fukumi, Yasue Mitsukura

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

This paper presents a new iterative algorithm for feature generation, which is approximately derived based on geometrical interpretation of the Fisher linear discriminant (FLD) analysis. In a field of pattern recognition or signal processing, the principal component analysis (PCA) is popular for data compression and feature extraction. Furthermore, iterative learning algorithms for obtaining eigenvectors in PCA have been presented in a field of pattern recognition, image analysis, and neural networks. Their effectiveness has been demonstrated in many applications. However, recently the FLD analysis has been used in many fields, especially face image analysis. The drawback of FLD is a long computational time in compression of a large-sized between-class covariance and within-class covariance matrices. Usually FLD has to carry out minimization of a within-class variance. However in this case the inverse matrix of the within-class covariance matrix cannot be obtained, since data dimension is higher than the number of data and then it includes many zero eigenvalues. In order to overcome this difficulty, a new iterative feature generation method, a simple FLD is introduced and its effectiveness is demonstrated.

本文言語English
ホスト出版物のタイトルProceedings of the Seventh IASTED International Conference on Signal and Image Processing, SIP 2005
編集者M.W. Marcellin
ページ342-346
ページ数5
出版ステータスPublished - 2005 12 1
外部発表はい
イベントSeventh IASTED International Conference on Signal and Image Processing, SIP 2005 - Honolulu, HI, United States
継続期間: 2005 8 152005 8 17

出版物シリーズ

名前Proceedings of the Seventh IASTED International Conference on Signal and Image Processing, SIP 2005

Other

OtherSeventh IASTED International Conference on Signal and Image Processing, SIP 2005
CountryUnited States
CityHonolulu, HI
Period05/8/1505/8/17

ASJC Scopus subject areas

  • Engineering(all)

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