Journal of South China University of Technology (Natural Science Edition) ›› 2010, Vol. 38 ›› Issue (7): 45-49,55.doi: 10．3969／j．issn．1000-565X．2010．07．008

• Computer Science & Technology • Previous Articles     Next Articles

Semi-Supervised Discriminant Analysis Method Based on Local Reconstruction and Global Preserving

Wei Jia1 Yang Chuang-xin2  Ma Qian-li1  Yu Guo-xian1   

  1. 1. School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,Guangdong,China; 2. School of Information Science,Guangdong University of Business Studies,Guangzhou 510320,Guangdong,China
  • Received:2009-10-09 Revised:2009-12-17 Online:2010-07-25 Published:2010-07-25
  • Contact: 韦佳（1982-）,男,讲师,博士,主要从事人工智能、机器学习等研究 E-mail: csjwei@scut.edu.cn
  • About author:韦佳（1982-）,男,讲师,博士,主要从事人工智能、机器学习等研究.
  • Supported by:

    广东省自然科学基金资助项目（07006474）; 华南理工大学中央高校基本科研业务费专项资金资助项目（2009ZM0189）

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Abstract:

Linear discriminant analysis （ LDA） can only make use of labeled samples. In order to overcome this shortcoming,a new semi-supervised discriminant analysis method based on local reconstruction and global preserving marked as LRGPSSDA is proposed. LRGPSSDA sets the edge weight of neighborhood graph by minimizing the local reconstruction error and preserves the global geometric structure of the sampled data set without destroying its local geometric structure. It is insensitive to the selection of neighborhood parameter,and the dimensionality of its projection subspace is independent of the number of sample classes. As compared with the existing semi-supervised discriminant analysis methods such as SDA and UDA,LRGPSSDA is of higher classification performance. The experimental results of YaleB and CMU PIE face database also demonstrate that LRGPSSDA is effective.

Key words: local reconstruction, global preserving, discriminant analysis, semi-supervised learning