A New Linear Initialization in SOM for Biomolecular Data
Authors: 
Antonino Fiannaca, Giuseppe Fatta, Alfonso Urso, Riccardo Rizzo, Salvatore GaglioAuthors Info & Claims
June 2009
Pages 177 - 187
Abstract
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.
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- A New Linear Initialization in SOM for Biomolecular Data
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Published In
June 2009
292 pages
ISBN:9783642025037
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 23 June 2009
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