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.
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Buonanno, A., di Grazia, L., Palmieri, F.A.N. (2016). Bayesian Clustering on Images with Factor Graphs in Reduced Normal Form. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_6
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DOI: https://doi.org/10.1007/978-3-319-33747-0_6
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