Feature Generation Method by Geometrical Interpretation of Fisher Linear Discriminant Analysis

• Oyama Tadahiro

  Department of Information & Science Intelligent Systems, The University of Tokushima

• Matsumura Yuji

  Department of Information & Science Intelligent Systems, The University of Tokushima

• Karungaru Stephen Githinji

  Department of Information & Science Intelligent Systems, The University of Tokushima

• Fukumi Minoru

  Department of Information & Science Intelligent Systems, The University of Tokushima

抄録

This paper presents a new algorithm for feature generation, which is derived based on geometrical interpretation of the fisher linear discriminant analysis (FLDA). This algorithm (Simple-FLDA) is an approximation algorithm that calculates eigenvectors sequentially by an easy iterative calculation by expressing the maximization of variance between classes and minimization of variance in each class without the use of matrix calculation. We carry out computer simulations about recognition of wrist motion patterns by EMG measured from wrist and personal authentications that use face images to verify the effectiveness of this technique. The result was compared with the result of principal component analysis (Simple-PCA).

収録刊行物

• 電気学会論文誌Ｃ（電子・情報・システム部門誌）

  電気学会論文誌Ｃ（電子・情報・システム部門誌） 127 (6), 831-836, 2007

  一般社団法人 電気学会

キーワード

• simple-FLDA
• simple-PCA
• pattern recognition
• EMG
• face recognition
• feature generation

詳細情報

• CRID: 1390282679583034752
• NII論文ID: 130000090459
• NII書誌ID: AN10065950
• DOI: 10.1541/ieejeiss.127.831
• ISSN: 13488155
• ISSN: 03854221
• NDL書誌ID: 8842679
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I8842679
• Web Site: http://www.jstage.jst.go.jp/article/ieejeiss/127/6/127_6_831/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可