Infinite sparse threshold unit networks

Authors:
Michiel Hermans;Benjamin Schrauwen
Affiliations:
ELIS Departement, Ghent University, Ghent, Belgium;ELIS Departement, Ghent University, Ghent, Belgium
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

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Abstract

In this paper we define a kernel function which is the dual space equivalent of infinitely large sparse threshold unit networks. We first explain how to couple a kernel function to an infinite recurrent neural network, and next we use this definition to apply the theory to sparse threshold unit networks. We validate this kernel function with a theoretical analysis and an illustrative signal processing task.