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Probabilistic matrix factorization from quantized measurements
2017
2017 International Joint Conference on Neural Networks (IJCNN)
We consider the problem of factorizing a matrix with discrete-valued entries as a product of two low-rank matrices. Under a probabilistic framework, we seek for the minimum mean-square error estimates of these matrices, using full Bayes and empirical Bayes approaches. In the first case, we devise an integration scheme based on the Gibbs sampler that accounts also for hyperparameter and noise variance estimation. A similar technique is used also for the latter case, where we combine Gibbs
doi:10.1109/ijcnn.2017.7965865
dblp:conf/ijcnn/BottegalS17
fatcat:fm4jd57gtfbepnnvbd2wncnmye