Kernel-based feature extraction under maximum margin criterion
نویسندگان
چکیده
منابع مشابه
Kernel-based feature extraction under maximum margin criterion
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 ...
متن کاملFeature extraction based on Laplacian bidirectional maximum margin criterion
Article history: Received 28 July 2008 Received in revised form 2 March 2009 Accepted 9 March 2009
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Dimensionality reduction is often recommended to handle high dimensional data before performing the tasks of visualization and classification. So far, large families of dimensionality reduction methods besides the supervised or the unsupervised, the linear or the nonlinear, the global or the local have been developed. In this paper, a maximum nonparametric margin projection (MNMP) method is put...
متن کاملComments on "Efficient and Robust Feature Extraction by Maximum Margin Criterion"
The goal of this comment is to first point out two loopholes in the paper by Li et al. (2006): 1) so-designed efficient maximal margin criterion (MMC) algorithm for small sample size (SSS) problem is problematic and 2) the discussion on the equivalence with the null-space-based methods in SSS problem does not hold. Then, we will present a really efficient MMC algorithm for SSS problem.
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ژورنال
عنوان ژورنال: Journal of Visual Communication and Image Representation
سال: 2012
ISSN: 1047-3203
DOI: 10.1016/j.jvcir.2011.08.002