A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neurons
The Self-Organizing Map (SOM) has applications like dimension reduction, data clustering, image analysis, and many others. In conventional SOM, the weights of the winner and its neighboring neurons are updated regardless of their distance from the input vector. In the proposed SOM, the farthest and...
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Elsevier
2014-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016814000970 |
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doaj-594a95854c5f4a0b82c3b644994c1bad2021-06-02T11:57:38ZengElsevierAlexandria Engineering Journal1110-01682014-12-0153482783110.1016/j.aej.2014.09.007A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neuronsVikas Chaudhary0R.S. Bhatia1Anil K. Ahlawat2National Institute of Technology (N.I.T.), Kurukshetra, Haryana, IndiaNational Institute of Technology (N.I.T.), Kurukshetra, Haryana, IndiaKrishna Institute of Engineering & Technology, Ghaziabad, U.P., IndiaThe Self-Organizing Map (SOM) has applications like dimension reduction, data clustering, image analysis, and many others. In conventional SOM, the weights of the winner and its neighboring neurons are updated regardless of their distance from the input vector. In the proposed SOM, the farthest and nearest neurons from among the 1-neighborhood of the winner neuron, and also the winning frequency of each neuron are found out and taken into account while updating the weight. This new SOM is applied to various input data sets and the learning performance is evaluated using three standard measurements. It is confirmed that modified SOM obtained a far better result and better effective mapping as compared to the conventional SOM, which reflects the input data distribution.http://www.sciencedirect.com/science/article/pii/S1110016814000970Self-Organizing Map (SOM)Farthest neuronNearest neuronWinning frequencyNeighborhood neurons |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Vikas Chaudhary R.S. Bhatia Anil K. Ahlawat |
| spellingShingle |
Vikas Chaudhary R.S. Bhatia Anil K. Ahlawat A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neurons Alexandria Engineering Journal Self-Organizing Map (SOM) Farthest neuron Nearest neuron Winning frequency Neighborhood neurons |
| author_facet |
Vikas Chaudhary R.S. Bhatia Anil K. Ahlawat |
| author_sort |
Vikas Chaudhary |
| title |
A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neurons |
| title_short |
A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neurons |
| title_full |
A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neurons |
| title_fullStr |
A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neurons |
| title_full_unstemmed |
A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neurons |
| title_sort |
novel self-organizing map (som) learning algorithm with nearest and farthest neurons |
| publisher |
Elsevier |
| series |
Alexandria Engineering Journal |
| issn |
1110-0168 |
| publishDate |
2014-12-01 |
| description |
The Self-Organizing Map (SOM) has applications like dimension reduction, data clustering, image analysis, and many others. In conventional SOM, the weights of the winner and its neighboring neurons are updated regardless of their distance from the input vector. In the proposed SOM, the farthest and nearest neurons from among the 1-neighborhood of the winner neuron, and also the winning frequency of each neuron are found out and taken into account while updating the weight. This new SOM is applied to various input data sets and the learning performance is evaluated using three standard measurements. It is confirmed that modified SOM obtained a far better result and better effective mapping as compared to the conventional SOM, which reflects the input data distribution. |
| topic |
Self-Organizing Map (SOM) Farthest neuron Nearest neuron Winning frequency Neighborhood neurons |
| url |
http://www.sciencedirect.com/science/article/pii/S1110016814000970 |
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