基于主曲线的微阵列数据分类

提出了一种基于主曲线(principal curves)的微阵列数据分类方法(PC)。主曲线是第一主成分的非线性推广，它是数据集合的“骨架”，数据集合是主曲线的“云”。基于主曲线的微阵列数据分类方法，首先利用专门设计的算法在训练数据集上计算出每类样本的主曲线，然后根据测试样本与各类样本主曲线距离的期望方差来确定测试样本所属的类别。实验结果表明，该分类方法在进行小样本微阵列数据分类时性能优于现有的方法。

Microarray Data Classification Based on Principal Curves

In this paper, a novel classifier was proposed to classify microarray data using principal curves. Principal curves are the non-linear generalization of principal components. Intuitively, a principal curve `passes through the middle of the data cloud'. As a kind of new classification technique,Principal Curve-based classifier (PC) involves a novel way of computing a principal curve for each class using the training data. A test sample is the class-label of the principal curve that is closest to it according to Expected Sctuared Error. Experimental results illustrate the performance of the PC is better than other existing approaches when a very small sample size of a microarray set is concerned.