NMF的数据分类方法在肿瘤分类上的应用

NMF-based method for data classification

在生物信息学中,一个重要的问题是基于微芯片技术将肿瘤分类到不同的类别中去。和许多传统的分类问题相比,这个问题的主要困难是基因空间的维数很高,而要分类的样本数量很小。非负矩阵分解(NMF)在微芯片数据聚类问题中已经成功地解决了这个问题。将非负矩阵分解拓展到数据分类,尤其是肿瘤分类中去取得了很好的效果。基于非负矩阵分解的方法有三个优点:良好的分类成绩,无参数和良好的可解释性。

In bioinformatics,an important task is to classify tumor samples into different classes based on microarray technology which enables people to monitor entire genome in a single chip using a system＇s approach.The key difficulty of this problem,compared with many traditional classification problems,is the high dimensionality in gene space and the small number of samples that will be classified.Non-negative Matrix Factorization（NMF） has coped with this difficulty successfully in microarray data clustering.NMF is extended to tumor classification and the result shows its competition.NMF-based method has three advantages：Good classification performance,parameter-independent and good interpretability.