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