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Discrete Independent Component Analysis (DICA) with Belief Propagation
May 26, 2015 Β· Declared Dead Β· π International Workshop on Machine Learning for Signal Processing
"No code URL or promise found in abstract"
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Authors
Francesco A. N. Palmieri, Amedeo Buonanno
arXiv ID
1505.06814
Category
cs.CV: Computer Vision
Cross-listed
cs.LG,
stat.ML
Citations
3
Venue
International Workshop on Machine Learning for Signal Processing
Last Checked
4 months ago
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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