非负矩阵分解
矩阵分解
微生物群
拉普拉斯矩阵
计算机科学
拉普拉斯算子
因式分解
正规化(语言学)
代表(政治)
计算生物学
人工智能
数据挖掘
模式识别(心理学)
数学
理论计算机科学
特征向量
生物
生物信息学
图形
算法
物理
数学分析
政治
量子力学
法学
政治学
作者
Xingpeng Jiang,Xiaohua Hu,Weiwei Xu
标识
DOI:10.1109/tcbb.2015.2440261
摘要
Microbiome datasets are often comprised of different representations or views which provide complementary information to understand microbial communities, such as metabolic pathways, taxonomic assignments, and gene families. Data integration methods including approaches based on nonnegative matrix factorization (NMF) combine multi-view data to create a comprehensive view of a given microbiome study by integrating multi-view information. In this paper, we proposed a novel variant of NMF which called Laplacian regularized joint non-negative matrix factorization (LJ-NMF) for integrating functional and phylogenetic profiles from HMP. We compare the performance of this method to other variants of NMF. The experimental results indicate that the proposed method offers an efficient framework for microbiome data analysis.
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