Multiple graph regularized nonnegative matrix factorization
Multiple graph regularized nonnegative matrix factorization
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DOI：
10.1016/j.patcog.2013.03.007
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Jim Jing-Yan Wang;H. Bensmail;Xin Gao
中科院分区：
文献类型：
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作者：
Jim Jing-Yan Wang;H. Bensmail;Xin Gao
非负矩阵分解（NMF）作为一种基于分量的数据表示方法得到了广泛的应用。为了克服NMF不能考虑数据集的流形结构的缺点，图正则化NMF（GrNMF）已经由Cai等人提出，通过构造亲和图并搜索尊重图结构的矩阵分解。选择图模型及其相应的参数是该策略的关键。这个过程通常通过交叉验证或离散网格搜索来进行，这是耗时的，并且容易过拟合。在本文中，我们提出了一个GrNMF，称为MultiGrNMF，其中的内在流形近似的线性组合的几个图形与不同的模型和参数的启发合奏流形正则化。在一个统一的目标函数内，同时确定图的分解度量和线性组合系数。它们在迭代算法中交替优化，从而产生一种新的数据表示算法。在蛋白质亚细胞定位任务和阿尔茨海默病诊断任务上的大量实验证明了该算法的有效性。
Non-negative matrix factorization (NMF) has been widely used as a data representation method based on components. To overcome the disadvantage of NMF in failing to consider the manifold structure of a data set, graph regularized NMF (GrNMF) has been proposed by Cai et al. by constructing an affinity graph and searching for a matrix factorization that respects graph structure. Selecting a graph model and its corresponding parameters is critical for this strategy. This process is usually carried out by cross-validation or discrete grid search, which are time consuming and prone to overfitting. In this paper, we propose a GrNMF, called MultiGrNMF, in which the intrinsic manifold is approximated by a linear combination of several graphs with different models and parameters inspired by ensemble manifold regularization. Factorization metrics and linear combination coefficients of graphs are determined simultaneously within a unified object function. They are alternately optimized in an iterative algorithm, thus resulting in a novel data representation algorithm. Extensive experiments on a protein subcellular localization task and an Alzheimer's disease diagnosis task demonstrate the effectiveness of the proposed algorithm.