The Comparison of Classification Model with Partial Least Square Based Dimension Reduction

  • Su-Fen Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 121)

Abstract

Dimension reduction is a very important technique to handle with the analysis of high dimensional data sets. Among various methods, Partial Least Square based Dimension Reduction (PLSDR) is one of the most effective one, which has been applied in many fields such as the analysis of microarray data. But the problem of choosing classification model with PLSDR has often been neglected, different classification models are applied arbitrary. Aim at this problem, the paper gives an examination of different classification model with PLSDR by intensive experiments. Furthermore, some interesting conclusions are presented.

Keywords

Support Vector Machine Partial Little Square Ordinary Little Square Linear Discriminant Analysis Dimension Reduction 
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 2011

Authors and Affiliations

  • Su-Fen Chen
    • 1
  1. 1.Department of Computer Science and TechnologyNanchang Institute of TechnologyNanchangChina

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