Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2007.891193
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. https://doi.org/10.1109/TNN.2007.891193
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
URI: http://scholarbank.nus.edu.sg/handle/10635/55600
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.

SCOPUSTM   
Citations

12
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

11
checked on Nov 21, 2017

Page view(s)

39
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


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