Discrete Independent Component Analysis (DICA) with Belief Propagation

Francesco A. N. Palmieri; Amedeo Buonanno

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arXiv · 1505.06814

Discrete Independent Component Analysis (DICA) with Belief Propagation

Francesco A. N. Palmieri·Amedeo Buonanno

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Abstract

We apply belief propagation to a Bayesian bipartite graph composed of discrete independent hidden variables and discrete visible variables. The network is the Discrete counterpart of Independent Component Analysis (DICA) and it is manipulated in a factor graph form for inference and learning. A full set of simulations is reported for character images from the MNIST dataset. The results show that the factorial code implemented by the sources contributes to build a good generative model for the data that can be used in various inference modes.

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BibTeX RIS

Francesco A. N. Palmieri, Amedeo Buonanno. 2015-05-26. Discrete Independent Component Analysis (DICA) with Belief Propagation. https://arxiv.org/abs/1505.06814

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