摘要:基因表达数据具有高维小样本特点，包含了大量与疾病无关的基因，对该类数据进行分析的首要步骤是特征选择.常见的特征选择方法需要有类标的数据，但样本类标获取往往比较困难.针对基因表达数据的特征选择问题，提出基于谱聚类的无监督特征选择思想FSSC（feature selection by spectral clustering）.FSSC对所有特征进行谱聚类，将相似性较高的特征聚成一类，定义特征的区分度与特征独立性，以二者之积度量特征重要性，从各特征簇选取代表性特征，构造特征子集.根据使用的不同谱聚类算法，得到FSSC-SD（FSSC based on standard deviation）、FSSC-MD（FSSC based on mean distance）和FSSC-ST（FSSC based on self-tuning）这3种无监督特征选择算法.以SVMs（support vector machines）和KNN（K-nearest neighbours）为分类器，在10个基因表达数据集上进行实验测试.结果表明，FSSC-SD、FSSC-MD和FSSC-ST算法均能选择到具有强分类能力的特征子集.



Abstract:Gene expression data usually comprise small number of samples with tens of thousands of genes. There are a large number of genes unrelated to diseases in this kind of data. The primary task is to detect those key essential genes when analyzing this kind of data. The common feature selection algorithms depend on labels of data, but it is very difficult to get labels for data. To overcome the challenges, especially for gene expression data, the unsupervised feature selection idea is proposed, named as FSSC (feature selection by spectral clustering). FSSC groups all of features into clusters by a spectral clustering algorithm, so that similar features are in same clusters. The feature discernibility and independence are defined, and the feature importance is defined as the product of its discernibility and independence. The representative feature is selected from each cluster to construct the feature subset. According to the spectral clustering algorithms used in FSSC, three kinds of unsupervised feature selection algorithms named as FSSC-SD (FSSC based on standard deviation), FSSC-MD (FSSC based on mean distance) and FSSC-ST (FSSC based on self-tuning) are developed. The SVM (support vector machines) and KNN (K-nearest neighbors) classifiers are adopted to test the performance of the selected feature subsets in experiments. Experimental results on 10 gene expression datasets show that FSSC-SD, FSSC-MD, and FSSC-ST algorithms can select powerful features to classify samples.



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