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Recently nonnegative Matrix Factorization (NMF) has been proven a powerful method in clustering analysis of gene expression data. There exist two popular loss functions for minimization in decomposition: one is Euclidean distance and the other generalized Kullback-Leibler divergence. Both loss functions can be derived from a linear model with additive noise, and the Euclidean distance loss corresponds...
Hierarchical clustering is a commonly used and valuable approach in clustering analysis. However it depends on the measure used to assess similarity between samples. Two frequently adopted distance measures are Euclidean distance (L2- norm) and city-block distance (L1-norm), and they do not take into account special characteristics of data at hand. In this paper, considering the nonnegativity of gene...
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