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Exploiting layerwise convexity of rectifier networks with sign constrained weights.

Authors :
An, Senjian
Boussaid, Farid
Bennamoun, Mohammed
Sohel, Ferdous
Source :
Neural Networks. Sep2018, Vol. 105, p419-430. 12p.
Publication Year :
2018

Abstract

By introducing sign constraints on the weights, this paper proposes sign constrained rectifier networks (SCRNs), whose training can be solved efficiently by the well known majorization–minimization (MM) algorithms. We prove that the proposed two-hidden-layer SCRNs, which exhibit negative weights in the second hidden layer and negative weights in the output layer, are capable of separating any number of disjoint pattern sets. Furthermore, the proposed two-hidden-layer SCRNs can decompose the patterns of each class into several clusters so that each cluster is convexly separable from all the patterns from the other classes. This provides a means to learn the pattern structures and analyse the discriminant factors between different classes of patterns. Experimental results are provided to show the benefits of sign constraints in improving classification performance and the efficiency of the proposed MM algorithm. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08936080
Volume :
105
Database :
Academic Search Index
Journal :
Neural Networks
Publication Type :
Academic Journal
Accession number :
131295616
Full Text :
https://doi.org/10.1016/j.neunet.2018.06.005