Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models

@inproceedings{Seeger2012FastVB,
  title={Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models},
  author={Matthias W. Seeger and Guillaume Bouchard},
  booktitle={AISTATS},
  year={2012}
}
Probabilistic matrix factorization methods aim to extract meaningful correlation structure from an incomplete data matrix by postulating low rank constraints. Recently, variational Bayesian (VB) inference techniques have successfully been applied to such large scale bilinear models. However, current algorithms are of the alternate updating or stochastic gradient descent type, slow to converge and prone to getting stuck in shallow local minima. While for MAP or maximum margin estimation… CONTINUE READING

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