NDLI: Information Maximization in a Linear Manifold Topographic Map

Content Provider	Springer Nature Link
Author	Adibi, Peyman Safabakhsh, Reza
Copyright Year	2009
Abstract	This article addresses the problem of unsupervised learning of multiple linear manifolds in a topology preserving neural map. The model finds simple linear estimations of the regions of the unknown data manifold. Each neuron of the map corresponds to a linear manifold whose basis and mean vectors and on- and off-manifold standard deviations must be learnt. The learning rules are derived based on competition between neurons and maximizing an approximation of the mutual information between the input and the output of each neuron. Neighborhood functions are also considered in the learning rules in order to develop the topology preserving property for the map. Considering two special density models for the input data, the optimal nonlinear input/output mappings of the neurons are found. Experimental results show a good performance for the proposed method on synthesized and practical problems compared with other relevant techniques.
Starting Page	155
Ending Page	178
Page Count	24
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	29
Issue Number	3
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2009-04-11
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Neural networks Unsupervised learning Linear manifold topographic map Adaptive subspace self-organizing map Principal component analysis Information maximization Entropy Computational Intelligence Complexity Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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