Enhanced spectral embedding with semi-supervised feature selection

Weiwei Du, Kiichi Urahama

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

抄録

We present a spectral embedding technique for semisupervised pattern classification. Importance scores of features are firstly evaluated with a semi-supervised feature selection algorithm by Zhao et al. Training data are then embedded into a low-dimensional space with a spectral mapping derived from the selected and weighted feature vectors with which test data are classified by the nearest neighbor rule. The performance of the proposed pattern classification algorithm is examined with synthetic and real dataseis.

本文言語英語
ホスト出版物のタイトル5th International Conference on Natural Computation, ICNC 2009
ページ129-133
ページ数5
DOI
出版ステータス出版済み - 12 1 2009
イベント5th International Conference on Natural Computation, ICNC 2009 - Tianjian, 中国
継続期間: 8 14 20098 16 2009

出版物シリーズ

名前5th International Conference on Natural Computation, ICNC 2009
1

その他

その他5th International Conference on Natural Computation, ICNC 2009
Country中国
CityTianjian
Period8/14/098/16/09

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

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