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Discrete Independent Component Analysis (DICA) with Belief Propagation

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.

preprint2015arXivOpen access
Francesco A. N. PalmieriAmedeo Buonanno
Computer VisionMachine Learning
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Open access2 authors2 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Francesco A. N. PalmieriAmedeo Buonanno

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