NMF-based Models for Tumor Clustering: A Systematic Comparison

摘要：

Nonnegative Matrix Factorization (NMF) is one of the famous unsupervised learning models. In this paper, we give a short survey on NMF-related models, including K-means, Probabilistic Latent Semantic Indexing etc. and present a new Posterior Probabilistic Clustering model, and compare their numerical experimental results on five real microarray data. The results show that i) NMF using with K-L divergence objective function has better clustering performance; ii) Our purposed PPC model is among the best.

关键词： Algorithm Nonnegative Matriz Factorization Microarray Comparison Bioinformatics

作者: Zhong-Yuan Zhang

作者单位: School of Statistics,Central University of Finance and Economics,Beijing 100080

会议类型: 国际会议

会议名称: The 3rd International Symposium on Optimization and System Biology(第三届最优化与系统生物学国际会议 OSB09)

会议地点: 张家界

会议语种:英文

页码: 41-47

在线出版日期: 2009-09-20（万方平台首次上网日期，不代表论文的发表时间）