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A new linear initialization in SOM for biomolecular data

Fiannaca, A., Di Fatta, G., Gaglio, S., Rizzo, R. and Urso, A. M. (2009) A new linear initialization in SOM for biomolecular data. Lecture Notes in Computer Science, 2009 (5488). pp. 177-187. ISSN 1611-3349

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To link to this article DOI: 10.1007/978-3-642-02504-4

Abstract/Summary

In the past decade, the amount of data in biological field has become larger and larger; Bio-techniques for analysis of biological data have been developed and new tools have been introduced. Several computational methods are based on unsupervised neural network algorithms that are widely used for multiple purposes including clustering and visualization, i.e. the Self Organizing Maps (SOM). Unfortunately, even though this method is unsupervised, the performances in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. In this paper we present a new initialization technique based on a totally connected undirected graph, that report relations among some intersting features of data input. Result of experimental tests, where the proposed algorithm is compared to the original initialization techniques, shows that our technique assures faster learning and better performance in terms of quantization error.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:6128
Additional Information:Selected revised papers from 'Computational Intelligence Methods for Bioinformatics and Biostatistics, 5th International Meeting, CIBB 2008, Vietri sul Mare, Italy, October 3-4, 2008. Computational Intelligence Methods for Bioinformatics and Biostatistics (ISBN:9783642025037)
Publisher:Springer Berlin / Heidelberg

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