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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Classifiability-based discriminatory projection pursuit.

Yu Su1, Shiguang Shan, Xilin Chen

  • 1GREYC, CNRS UMR6072, University of Caen, Caen 14032, France. yu.su@unicaen.fr

IEEE Transactions on Neural Networks
|October 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces classifiability-based discriminatory projection pursuit (CDPP), a novel method for discriminative linear feature extraction. CDPP overcomes limitations of Fisher's linear discriminant (FLD) by using boundary samples and AdaBoost learning for improved generalizability.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Fisher's linear discriminant (FLD) is a standard technique for linear feature extraction in visual computation.
  • Traditional FLD has limitations that hinder its performance in certain complex tasks.
  • A need exists for advanced methods that improve upon FLD's capabilities.

Purpose of the Study:

  • To propose a new computational paradigm, classifiability-based discriminatory projection pursuit (CDPP), for discriminative linear feature extraction.
  • To address the limitations inherent in traditional FLD and its variants.
  • To develop a method that enhances generalizability in feature extraction.

Main Methods:

  • CDPP involves two main steps: constructing a candidate projection set (CPS) and pursuing discriminatory projections.
  • Candidate projections are generated using nearest between-class boundary samples.
  • Discriminatory projections are efficiently learned using classifiability-based AdaBoost from the CPS.

Main Results:

  • The proposed CDPP paradigm effectively extracts discriminative linear features.
  • CDPP demonstrates improved generalizability compared to traditional FLD.
  • Experimental results on synthetic and real datasets validate the method's effectiveness.

Conclusions:

  • CDPP offers a robust alternative to FLD for discriminative linear feature extraction.
  • The method's reliance on boundary samples and AdaBoost learning contributes to its superior performance.
  • CDPP shows significant promise for various visual computation and machine learning applications.