Dimension Reduction Based on Modified Maximum Margin Criterion for Tumor Classification

Shanwen Zhang, Rongzhi Jing
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引用次数: 4

Abstract

Based on Maximum margin criterion (MMC), a new algorithm, named modified MMC, is proposed for supervised dimensionality reduction in this paper. The algorithm aims at learning a linear transformation, and aims at maximizing the average margin between classes in the projected space. After projecting, the considered pair wise points within the same class are as close as possible, while those between different classes are as far as possible. The performance on two gene expression profiles datasets demonstrates the effectiveness of the proposed method.
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基于改进最大切缘准则的降维肿瘤分类
基于最大余量准则(MMC),提出了一种新的有监督降维算法——改进的MMC算法。该算法的目标是学习一个线性变换,并以最大化投影空间中类之间的平均边际为目标。投影后,同一类内考虑的对明智点尽可能接近,而不同类之间考虑的对明智点尽可能远。在两个基因表达谱数据集上的性能证明了该方法的有效性。
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