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Comments on “Efficient and Robust Feature Extraction by Maximum Margin Criterion”
"... Abstract—The goal of this comment is to first point out two loopholes in the paper by Li et al. (2006): 1) sodesigned efficient maximal margin criterion (MMC) algorithm for small sample size (SSS) problem is problematic and 2) the discussion on the equivalence with the nullspacebased methods in S ..."
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Abstract—The goal of this comment is to first point out two loopholes in the paper by Li et al. (2006): 1) sodesigned efficient maximal margin criterion (MMC) algorithm for small sample size (SSS) problem is problematic and 2) the discussion on the equivalence with the nullspacebased methods in SSS problem does not hold. Then, we will present a really efficient MMC algorithm for SSS problem. Index Terms—Efficient algorithm, equivalence, maximal margin criterion (MMC), null space, small sample size (SSS) problem. I. ORGANIZATION AND PREPARATION Organization: In this section, we will give some notations and a brief review of maximum margin criterion (MMC) [3] and point out the two loopholes. In Section II, we will propose a really efficient MMC, and then, conclude this comment in Section III. Let the training set be composed of ™ classes gIYgPY FFFYg™, the �th class have � � training samples, and � � � denote the �th hdimensional sample from the �th class. In total, there will be � a �aI � � training samples. In applications such as face recognition, the small sample size (SSS) problem often takes place, namely, h) �. The withinclass scatter matrix ƒ � and betweenclass scatter matrix ƒ ˜ can be denoted as ƒ � a I ƒ ˜ a I
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, 2009
"... This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. ANMM4CBR: a casebased reasoning method for gene expression data classification ..."
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This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text (HTML) versions will be made available soon. ANMM4CBR: a casebased reasoning method for gene expression data classification