Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
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DOI：
10.1093/bioinformatics/btm134
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发表时间：
2007-06-15
期刊：
影响因子：
5.8
通讯作者：
Park, Haesun
中科院分区：
文献类型：
--
作者：
Kim, Hyunsoo;Park, Haesun
动机：许多实际的模式识别问题需要非负约束。例如，数字图像中的像素和生物信息学中的化学浓度是非负的。稀疏非负矩阵分解（NMF）在低维空间中逼近高维数据时，需要控制NMF中非负基矩阵或非负系数矩阵的稀疏程度时非常有用。在本文中，我们引入了一种新的稀疏NMF公式，并展示了新公式如何通过交替非负性导致收敛的稀疏NMF算法，约束最小二乘法我们将我们的稀疏NMF算法应用于癌症类发现和基因表达数据分析，并对所获得的结果进行生物学分析。我们的实验结果表明，所提出的稀疏NMF算法往往取得更好的聚类性能与更短的计算时间相比，其他现有的NMF算法。
Motivation: Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Sparse non-negative matrix factorizations (NMFs) are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approximating high-dimensional data in a lower dimensional space.Results: In this article, we introduce a novel formulation of sparse NMF and show how the new formulation leads to a convergent sparse NMF algorithm via alternating non-negativity-constrained least squares. We apply our sparse NMF algorithm to cancer-class discovery and gene expression data analysis and offer biological analysis of the results obtained. Our experimental results illustrate that the proposed sparse NMF algorithm often achieves better clustering performance with shorter computing time compared to other existing NMF algorithms.