Bayesian Semi Non-negative Matrix Factorisation
نویسندگان
چکیده
Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when data, sources and mixing coefficients are constrained to be positive-valued. The method has recently been extended to allow for negative-valued data and sources in the form of Semiand Convex-NMF. In this paper, we re-elaborate Semi-NMF within a full Bayesian framework. This provides solid foundations for parameter estimation and, importantly, a principled method to address the problem of choosing the most adequate number of sources to describe the observed data. The proposed Bayesian Semi-NMF is preliminarily evaluated here in a real neuro-oncology problem.
منابع مشابه
Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation
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2 Model discussion 6 2.1 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Model selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Initialisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.4 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.5 Code . . . ...
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