We present a Bayesian treatment of non-negative matrix fac- torization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs ...
Missing: semi | Show results with:semi
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To address these limitations, we propose a Bayesian semi-nonnegative matrix tri- factorization method to identify pathways associated with cancer phenotypes ...
In this paper, we propose a semi nonnegative matrix factorization algorithm based on Bayesian probability model. Using semi supervised learning method, the ...
Dec 1, 2017 · We propose a Bayesian (semi-)nonnegative matrix factorization model for human cancer genomic data, where the biological prior knowledge ...
Missing: factorisation. | Show results with:factorisation.
Abstract. Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when data, sources and mixing.
Non-negative matrix factorization (NMF or NNMF), also non-negative matrix approximation is a group of algorithms in multivariate analysis and linear algebra ...
Apr 17, 2024 · In this paper, we introduce a novel methodology for overfitted Bayesian NMF models using compressive hyperpriors that force unneeded factors down to negligible ...
May 27, 2009 · We describe nonnegative matrix factorisation (NMF) with a Kullback-Leibler (KL) error measure in a statistical framework, with a hierarchical generative model.
Missing: semi | Show results with:semi
We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient ...
Bayesian semi-nonnegative matrix tri-factorization to identify pathways ...
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Aug 20, 2019 · We propose a Bayesian method to identify associations between cancer phenotypes (e.g., molecular subtypes) and pathways from human cancer ...