An improved locality sensitive discriminant analysis approach for feature extraction

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• 作者：Yugen Yi (1) (3)
  Baoxue Zhang (3)
  Jun Kong (1) (2)
  Jianzhong Wang (4)
  
  1. College of Computer Science and Information Technology ; Northeast Normal University ; Changchun ; China
  3. School of Mathematics and Statistics ; Northeast Normal University ; Changchun ; China
  2. Key Laboratory of Intelligent Information Processing of Jilin Universities ; Northeast Normal University ; Changchun ; China
  4. National Engineering Laboratory for Druggable Gene and Protein Screening ; Northeast Normal University ; Changchun ; China
  
• 关键词：Feature extraction ; Outliers ; ILSDA ; Face image recognition
• 刊名：Multimedia Tools and Applications
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：74
• 期：1
• 页码：85-104
• 全文大小：1,764 KB
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• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721

文摘

Recently, Locality Sensitive Discriminant Analysis (LSDA) has been proposed as an efficient feature extraction approach. By analyzing the local manifold structure of high-dimensional data, LSDA can obtain a subspace in which the nearby points with the same label are close to each other while the nearby points with different labels are far apart. However, because LSDA only takes the local information into consideration, it may fail to deal with the data set which contains some outliers. In order to address this limitation, a new algorithm called Improved Locality Sensitive Discriminant Analysis (ILSDA) is proposed in this paper. By integrating the intra-class scatter matrix into our algorithm, ILSDA can not only preserve the local discriminant neighborhood structure of the data, but also pull the outlier samples more close to their class centers, which makes it outperform the original LSDA and some other state of the art algorithms. Extensive experimental results on several publicly available image datasets show the feasibility and effectiveness of our proposed approach.