Feature generation by simple FLD

Minora Fukumi, Yasue Mitsukura

研究成果: Conference contribution


This paper presents a new algorithm for feature generation, which is approximately derived based on geometrical interpretation of the Fisher linear discriminant analysis. In a field of pattern recognition or signal processing, the principal component analysis (PCA) is often used for data compression and feature extraction. Furthermore, iterative learning algorithms for obtaining eigen-vectors have been presented in pattern recognition and image analysis. Their effectiveness has been demonstrated on computational time and pattern recognition accuracy in many applications. However, recently the Fisher linear discriminant (FLD) analysis has been used in such a field, especially face image analysis. The drawback of FLD is a long computational time in compression of large-sized between-class 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.

ホスト出版物のタイトルKnowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
出版社Springer Verlag
ISBN(印刷版)3540288945, 9783540288947
出版ステータスPublished - 2005 1 1
イベント9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia
継続期間: 2005 9 142005 9 16


名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3681 LNAI


Other9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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