Rank-One NMF-Based Initialization for NMF and Relative Error Bounds under a Geometric Assumption

27 Dec 2016 Zhaoqiang Liu Vincent Y. F. Tan

We propose a geometric assumption on nonnegative data matrices such that under this assumption, we are able to provide upper bounds (both deterministic and probabilistic) on the relative error of nonnegative matrix factorization (NMF). The algorithm we propose first uses the geometric assumption to obtain an exact clustering of the columns of the data matrix; subsequently, it employs several rank-one NMFs to obtain the final decomposition... (read more)

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