摘要: We propose the infinite non-negative matrix factorization (inmf) which assumes a potentially unbounded number of components in Bayesian nmf model. devise an inference scheme based on Gibbs sampling conjunction with Metropolis-Hastings moves that admits cross-dimensional exploration posterior density. The approach can effectively establish model order for at less computational cost than existing approaches such as thermodynamic integration and reversible jump Markov chain Monte Carlo schemes. On synthetic real data we demonstrate success (inmf).
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