Consistent feature selection and its application to face recognition

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• 作者：Feng Pan (1)
  Guangwei Song (1)
  Xiaobing Gan (1)
  Qiwei Gu (1)
  
• 关键词：Feature selection ; Pattern recognition ; Laplacian matrix ; Eigen ; decomposition
• 刊名：Journal of Intelligent Information Systems
• 出版年：2014
• 出版时间：October 2014
• 年：2014
• 卷：43
• 期：2
• 页码：307-321
• 全文大小：797 KB
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• 作者单位：Feng Pan (1)
  Guangwei Song (1)
  Xiaobing Gan (1)
  Qiwei Gu (1)
  
  1. College of Management, Shenzhen University, Shenzhen, 518060, Guangdong, China
  
• ISSN：1573-7675

文摘

In this paper we consider feature selection for face recognition using both labeled and unlabeled data. We introduce the weighted feature space in which the global separability between different classes is maximized and the local similarity of the neighboring data points is preserved. By integrating the global and local structures, a general optimization framework is formulated. We propose a simple solution to this problem, avoiding the matrix eigen-decomposition procedure which is often computationally expensive. Experimental results demonstrate the efficacy of our approach and confirm that utilizing labeled and unlabeled data together does help feature selection with small number of labeled samples.