Presentation	2006-06-16 Extraction of independent features in the hidden layer of Neural Networks Kentaro MATSUKURA, Noboru MURATA,
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Abstract(in English)	Neural Networks (NN) are simplified models of neural processing in a human brain. Its applications varies in wide areas of machine learning. Each output of the, unit consisting the hidden layer in a NN, is often similar when learning the network. On the other hand, nonlinear sparse coding can be performed by expressing the relation between the input and the output in the hidden layer. We can expect to extract information of the I/O from each unit of the hidden layer by this performance. We propose a method to extract independent features from the hidden layer in NNs and investigated it by a simple experiment.
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Keyword(in English)	Neural Network / Sparse Codeing
Paper #	NC2006-33
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Conference Information
Committee	NC
Conference Date	2006/6/9(1days)
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Paper Information
Registration To	Neurocomputing (NC)
Language	JPN
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Title (in English)	Extraction of independent features in the hidden layer of Neural Networks
Sub Title (in English)
Keyword(1)	Neural Network
Keyword(2)	Sparse Codeing
1st Author's Name	Kentaro MATSUKURA
1st Author's Affiliation	Waseda University()
2nd Author's Name	Noboru MURATA
2nd Author's Affiliation	Waseda University
Date	2006-06-16
Paper #	NC2006-33
Volume (vol)	vol.106
Number (no)	102
Page	pp.pp.-
#Pages	5
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