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Supervised Learning for Classification

  • Hongyu Li
  • Wenbin Chen
  • I-Fan Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)

Abstract

Supervised local tangent space alignment is proposed for data classification in this paper. It is an extension of local tangent space alignment, for short, LTSA, from unsupervised to supervised learning. Supervised LTSA is a supervised dimension reduction method. It make use of the class membership of each data to be trained in the case of multiple classes, to improve the quality of classification. Furthermore we present how to determine the related parameters for classification and apply this method to a number of artificial and realistic data. Experimental results show that supervised LTSA is superior for classification to other popular methods of dimension reduction when combined with simple classifiers such as the k-nearest neighbor classifier.

Keywords

Feature Space Dimension Reduction Locally Linear Embedding Linear Embedding Membership Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hongyu Li
    • 1
  • Wenbin Chen
    • 2
  • I-Fan Shen
    • 1
  1. 1.Department of Computer Science and EngineeringFudan UniversityShanghaiChina
  2. 2.Department of MathematicsFudan UniversityShanghaiChina

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