Kernel-based feature extraction under maximum margin criterion

@article{Wang2012KernelbasedFE,
  title={Kernel-based feature extraction under maximum margin criterion},
  author={Jiangping Wang and Jieyan Fan and Huanghuang Li and Dapeng Wu},
  journal={J. Visual Communication and Image Representation},
  year={2012},
  volume={23},
  pages={53-62}
}
In this paper, we study the problem of feature extraction for pattern classification applications. RELIEF is considered as one of the best-performed algorithms for assessing the quality of features for pattern classification. Its extension, local feature extraction (LFE), was proposed recently and was shown to outperform RELIEF. In this paper, we extend LFE to the nonlinear case, and develop a new algorithm called kernel LFE (KLFE). Compared with other feature extraction algorithms, KLFE enjoys… CONTINUE READING

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C

D. Newman, S. Hettich, C. Blake
Merz, UCI repository of machine learning databases • 1998
View 3 Excerpts
Highly Influenced

A Practical Approach to Feature Selection

View 4 Excerpts
Highly Influenced

Feature extraction through local learning

Statistical Analysis and Data Mining • 2009
View 1 Excerpt

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