NMF-based Models for Tumor Clustering: A Systematic Comparison

被引：0

作者：

Zhang, Zhong-Yuan ^{[1
]}

机构：

[1] Cent Univ Finance & Econ, Sch Stat, Beijing 100080, Peoples R China

来源：

OPTIMIZATION AND SYSTEMS BIOLOGY | 2009年 / 11卷

关键词：

Algorithm; Nonnegative Matrix Factorization; Microarray; Comparison; Bioinformatics;

D O I：

暂无

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

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.

引用

页码：41 / 47

页数：7

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