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Applications of Projected Belief Networks (PBN)

: Baggenstoss, Paul M.


Bugallo, Mónica F. (General Chair) ; Institute of Electrical and Electronics Engineers -IEEE-; European Association for Signal Processing -EURASIP-:
27th European Signal Processing Conference, EUSIPCO 2019 : A Coruña, Spain, September 2-6, 2019
Piscataway, NJ: IEEE, 2019
ISBN: 978-9-0827-9703-9
ISBN: 978-90-827970-2-2
ISBN: 978-1-5386-7300-3
European Signal Processing Conference (EUSIPCO) <27, 2019, A Coruña/Spain>
Conference Paper
Fraunhofer FKIE ()

The projected belief network (PBN) is a layered generative network, with tractable likelihood function (LF) that can be trained by gradient ascent as a probability density function (PDF) estimator and classifier. The PBN is derived from a feed-forward neural network (FF-NN) by finding the generative network that implements the probability distribution with maximum entropy (MaxEnt) consistent with the knowledge of the distribution at the output of the FF-NN. The FF-NN, from which the PBN is derived, is a complementary feature extractor that exactly recovers the PBN's hidden variables. This paper presents a multi-layer PBN and a deterministic PBN that are tested using a subset of MNIST data set. When the deterministic PBN is combined with the dual FF-NN, it forms an auto-encoder that achieves much lower reconstruction error on testing data than the equivalent conventional network and functions significantly better as a classifier.