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Flexible non-greedy discriminant subspace feature extraction.

Henghao Zhao1, Liyong Fu2, Zhigang Gao3

  • 1College of Information Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu 210037, PR China; Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 8, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible non-greedy discriminant subspace method, enhancing feature extraction for dimensionality reduction. The new approach improves upon existing methods by using Lp-norm distance, showing effectiveness in image database experiments.

Keywords:
Intra-class dispersionL-norm inter-class dispersionL-norm-based non-greedy discriminant analysisRobust distance measurement

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Area of Science:

  • Computer Science
  • Machine Learning
  • Pattern Recognition

Background:

  • Non-greedy linear discriminant analysis (NLDA-L1) is used for feature extraction and dimensionality reduction.
  • NLDA-L1 often yields suboptimal performance due to its reliance on L1-norm distance measurements.

Purpose of the Study:

  • To propose a flexible non-greedy discriminant subspace feature extraction method.
  • To extend NLDA-L1 by optimizing the ratio of Lp-norm inter-class to intra-class dispersion.

Main Methods:

  • Developed a novel feature extraction technique extending NLDA-L1.
  • Maximized the ratio of Lp-norm inter-class dispersion to intra-class dispersion.
  • Proposed an iterative algorithm for solving the objective function and provided theoretical analysis.

Main Results:

  • Experimental results on image databases demonstrated the method's effectiveness.
  • The proposed Lp-norm based approach outperformed previous L1-norm methods.

Conclusions:

  • The flexible non-greedy discriminant subspace method offers improved feature extraction.
  • The new method provides a more robust approach to dimensionality reduction in image analysis.