Bayesian clustering implemented on a small Factor Graph is utilized in this work to perform associative recall and pattern recognition on images. The network is trained using a maximum likelihood algorithm on images from a standard data set. The two-class labels are fused with the image data into a unique hidden variable. Performances are evaluated in terms of Kullback-Leibler (KL) divergence between forward and backward messages for images and labels. These experiments reveal the nature of the representation that the learning algorithm builds in the hidden variable.
Bayesian clustering on images with factor graphs in reduced normal form
Palmieri, Francesco A. N.
2016
Abstract
Bayesian clustering implemented on a small Factor Graph is utilized in this work to perform associative recall and pattern recognition on images. The network is trained using a maximum likelihood algorithm on images from a standard data set. The two-class labels are fused with the image data into a unique hidden variable. Performances are evaluated in terms of Kullback-Leibler (KL) divergence between forward and backward messages for images and labels. These experiments reveal the nature of the representation that the learning algorithm builds in the hidden variable.File in questo prodotto:
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