Abstract: 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.

Key words: Non-negative Matrix Factorization（NMF）, microarray, data classification

摘要： 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。

关键词: 非负矩阵分解, 微芯片, 数据分类