Algorithms and applications for approximate nonnegative matrix factorization
Algorithms and applications for approximate nonnegative matrix factorization
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DOI：
10.1016/j.csda.2006.11.006
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发表时间：
2007-09-15
影响因子：
1.8
通讯作者：
Plemmons, Robert J.
中科院分区：
文献类型：
--
作者：
Berry, Michael W.;Browne, Murray;Plemmons, Robert J.
介绍了低秩近似非负矩阵分解（NMF）算法在文本挖掘和光谱数据分析领域中的特征提取和识别的发展和应用。基于稀疏性和光滑性约束的混合方法的演化和收敛性质的非负矩阵因子进行了讨论。NMF输出在特定环境下的可解释性提供了沿着的机会，为未来的工作在大规模和时变数据集的NMF算法的修改。(c)2006 Elsevier B.V.保留所有权利。
The development and use of low-rank approximate nonnegative matrix factorization (NMF) algorithms for feature extraction and identification in the fields of text mining and spectral data analysis are presented. The evolution and convergence properties of hybrid methods based on both sparsity and smoothness constraints for the resulting nonnegative matrix factors are discussed. The interpretability of NMF outputs in specific contexts are provided along with opportunities for future work in the modification of NMF algorithms for large-scale and time-varying data sets. (c) 2006 Elsevier B.V. All rights reserved.