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Sensitivity Analysis with Cross-Validation for Feature Selection and Manifold Learning

  • Cuixian Chen
  • Yishi Wang
  • Yaw Chang
  • Karl Ricanek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7367)

Abstract

The performance of a learning algorithm is usually measured in terms of prediction error. It is important to choose an appropriate estimator of the prediction error. This paper analyzes the statistical properties of the K-fold cross-validation prediction error estimator. It investigates how to compare two algorithms statistically. It also analyzes the sensitivity to the changes in the training/test set. Our main contribution is to experimentally study the statistical property of repeated cross-validation to stabilize the prediction error estimation, and thus to reduce the variance of the prediction error estimator. Our simulation results provide an empirical evidence to this conclusion. The experimental study has been performed on PAL dataset for age estimation task.

Keywords

Feature Selection Prediction Error Support Vector Regression Mean Absolute Error Locality Preserve Projection 
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 2012

Authors and Affiliations

  • Cuixian Chen
    • 1
  • Yishi Wang
    • 1
  • Yaw Chang
    • 1
  • Karl Ricanek
    • 1
  1. 1.University of North Carolina WilmingtonUSA

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