NDLI: Beyond second-order statistics for learning: A pairwise interaction model for entropy estimation

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Beyond second-order statistics for learning: A pairwise interaction model for entropy estimation

Content Provider	Springer Nature Link
Author	Erdogmus, Deniz Principe, Jose C. Hild, Kenneth E.
Copyright Year	2002
Abstract	Second order statistics have formed the basisof learning and adaptation due to its appealand analytical simplicity. On the other hand,in many realistic engineering problemsrequiring adaptive solutions, it is notsufficient to consider only the second orderstatistics of the underlying distributions. Entropy, being the average information contentof a distribution, is a better-suited criterionfor adaptation purposes, since it allows thedesigner to manipulate the information contentof the signals rather than merely their power. This paper introduces a nonparametric estimatorof Renyi's entropy, which can be utilized inany adaptation scenario where entropy plays arole. This nonparametric estimator leads to aninteresting analogy between learning andinteracting particles in a potential field. Itturns out that learning by second orderstatistics is a special case of thisinteraction model for learning. We investigatethe mathematical properties of thisnonparametric entropy estimator, provide batchand stochastic gradient expressions foroff-line and on-line adaptation, and illustratethe performance of the corresponding algorithmsin examples of supervised and unsupervisedtraining, including time-series prediction andICA.
Starting Page	85
Ending Page	108
Page Count	24
File Format	PDF
ISSN	15677818
Journal	Natural Computing
Volume Number	1
Issue Number	1
e-ISSN	15729796
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2002-01-01
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Processor Architectures Theory of Computation Artificial Intelligence (incl. Robotics) Evolutionary Biology Nonlinear Dynamics, Complex Systems, Chaos, Neural Networks
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications

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