Feature Extraction for Regression Problems and an Example Application for Pose Estimation of a Face

  • Nojun Kwak
  • Sang-Il Choi
  • Chong-Ho Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)

Abstract

In this paper, we propose a new feature extraction method for regression problems. It is a modified version of linear discriminant analysis (LDA) which is a very successful feature extraction method for classification problems. In the proposed method, the between class and the within class scatter matrices in LDA are modified so that they fit in regression problems. The samples with small differences in the target values are used to constitute the within class scatter matrix while the ones with large differences in the target values are used for the between class scatter matrix. We have applied the proposed method in estimating the head pose and compared the performance with the conventional feature extraction methods.

Keywords

Regression Feature extraction Dimensionality reduction LDA 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Nojun Kwak
    • 1
  • Sang-Il Choi
    • 2
  • Chong-Ho Choi
    • 2
  1. 1.Division of Electrical & Computer EngineeringAjou UniversitySuwonKorea
  2. 2.School of Electrical Engineering and Computer ScienceSeoul National UniversitySeoulKorea

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