Nonsmooth nonnegative matrix factorization (nsNMF)
Nonsmooth nonnegative matrix factorization (nsNMF)
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DOI：
10.1109/tpami.2006.60
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发表时间：
2006-03-01
影响因子：
23.6
通讯作者：
Pascual-Marqui, RD
中科院分区：
文献类型：
--
作者：
Pascual-Montano, A;Carazo, JM;Pascual-Marqui, RD
我们提出了一种新的非负矩阵分解模型，旨在寻找非负多元数据项的局部，基于部分的表示。与经典的非负矩阵分解（NMF）技术不同，这个新模型，被称为“非光滑非负矩阵分解”（nsNMF），对应于一个明确的成本函数的优化，该函数被设计成以非光滑的形式显式地表示稀疏性，由单个参数控制。一般来说，这种方法产生一组基和编码向量，它们不仅能够表示原始数据，而且还能提取高度本地化的模式，这些模式通常有助于提高可解释性。用几个数据集说明了这种新方法的性质。与先前发表的方法比较表明，新的nsNMF方法在保持数据的忠实性方面具有一定的优势，在估计基和编码向量上都实现了高度的稀疏性，并且在因素的可解释性方面具有更好的可解释性。
We propose a novel nonnegative matrix factorization model that aims at finding localized, part-based, representations of nonnegative multivariate data items. Unlike the classical nonnegative matrix factorization (NMF) technique, this new model, denoted '' nonsmooth nonnegative matrix factorization '' (nsNMF), corresponds to the optimization of an unambiguous cost function designed to explicitly represent sparseness, in the form of nonsmoothness, which is controlled by a single parameter. In general, this method produces a set of basis and encoding vectors that are not only capable of representing the original data, but they also extract highly localized patterns, which generally lend themselves to improved interpretability. The properties of this new method are illustrated with several data sets. Comparisons to previously published methods show that the new nsNMF method has some advantages in keeping faithfulness to the data in the achieving a high degree of sparseness for both the estimated basis and the encoding vectors and in better interpretability of the factors.