Please use this identifier to cite or link to this item:
Title: Determination of the number of principal directions in a biologically plausible PCA model
Authors: Lv, J.C.
Yi, Z.
Tan, K.K. 
Keywords: Biologically plausible model
Generalized Hebbian algorithm (GHA) learning algorithm
Intrinsic dimensionality
Principal component analysis (PCA)
Issue Date: May-2007
Source: Lv, J.C., Yi, Z., Tan, K.K. (2007-05). Determination of the number of principal directions in a biologically plausible PCA model. IEEE Transactions on Neural Networks 18 (3) : 910-916. ScholarBank@NUS Repository.
Abstract: Adaptively determining an appropriate number of principal directions for principal component analysis (PCA) neural networks is an important problem to address when one uses PCA neural networks for online feature extraction. In this letter, inspired from biological neural networks, a single-layer neural network model with lateral connections is proposed which uses an improved generalized Hebbian algorithm (GHA) to address this problem. In the proposed model, the number of principal directions can be adaptively determined to approximate the intrinsic dimensionality of the given data set so that the dimensionality of the data set can be reduced to approach the intrinsic dimensionality to any required precision through the network. © 2007 IEEE.
Source Title: IEEE Transactions on Neural Networks
ISSN: 10459227
DOI: 10.1109/TNN.2007.891193
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Mar 7, 2018


checked on Jan 24, 2018

Page view(s)

checked on Mar 11, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.