Robust Bayesian Nonnegative Matrix Factorization with Implicit Regularizers

no code implementations • 22 Aug 2022 • Jun Lu, Christine P. Chai

We introduce a probabilistic model with implicit norm regularization for learning nonnegative matrix factorization (NMF) that is commonly used for predicting missing values and finding hidden patterns in the data, in which the matrix factors are latent variables associated with each data dimension.

Bayesian Inference