Discrete Independent Component Analysis (DICA) with Belief Propagation

Autor:	Palmieri, Francesco A. N., Buonanno, Amedeo
Rok vydání:	2015
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Sumbitted for publication (May 2015)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1505.06814 Zobrazit plný text záznamu View this record from Arxiv