Piecewise one dimensional Self Organizing Map for fast feature extraction

It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition field. Face recognition databases are usually high dimensionality, especially when limited training samples are available for each subject. Traditional techniques perform dimensiona...

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Published in:2010 10th International Conference on Intelligent Systems Design and Applications pp. 633 - 638
Main Author: Sagheer, A
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2010
Subjects:
Accuracy
computation complexity
face recognition
Feature extraction
Image recognition
Neurons
principal rows analysis
self organizing maps
Testing
Training
ISBN:1424481341, 9781424481347
ISSN:2164-7143
Online Access:Get full text
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Abstract It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition field. Face recognition databases are usually high dimensionality, especially when limited training samples are available for each subject. Traditional techniques perform dimensionality reduction are unable to solve this problem smoothly, which makes feature extraction task much difficult. As such, a novel method performs feature extraction and dimensionality reduction for high-dimensional data is needed. In this paper, a new algorithm for traditional Self Organizing Map (SOM) is presented to cope with this problem with low computation cost. It is shown here that the computation cost of the proposed approach, comparing to traditional SOM is reduced into O(d 1 + d 2 +...+ d N ) instead of O(d 1 × d 2 ×... × d N ), where d j is the number of neurons through a dimension d j of the feature map. Experiments are carried out using benchmark database show that the proposed algorithm is a good alternate to traditional SOM, especially, when high-dimensional feature space is desired.
AbstractList It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition field. Face recognition databases are usually high dimensionality, especially when limited training samples are available for each subject. Traditional techniques perform dimensionality reduction are unable to solve this problem smoothly, which makes feature extraction task much difficult. As such, a novel method performs feature extraction and dimensionality reduction for high-dimensional data is needed. In this paper, a new algorithm for traditional Self Organizing Map (SOM) is presented to cope with this problem with low computation cost. It is shown here that the computation cost of the proposed approach, comparing to traditional SOM is reduced into O(d 1 + d 2 +...+ d N ) instead of O(d 1 × d 2 ×... × d N ), where d j is the number of neurons through a dimension d j of the feature map. Experiments are carried out using benchmark database show that the proposed algorithm is a good alternate to traditional SOM, especially, when high-dimensional feature space is desired.
Author Sagheer, A
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  organization: Math. Dept., South Valley Univ., Aswan, Egypt
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Snippet It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition field. Face recognition databases are...
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StartPage 633
SubjectTerms Accuracy
computation complexity
face recognition
Feature extraction
Image recognition
Neurons
principal rows analysis
self organizing maps
Testing
Training
Title Piecewise one dimensional Self Organizing Map for fast feature extraction
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