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The Self Organizing Map (SOM) is one of the most widely used neural network paradigm based on unsupervised competitive learning. However. the learning algorithm introduced by Kohonen is very slow when... the size of the map is large. This slowness is caused by seeking about the best node among "all" the map nodes which tunes to "each" input sample. In this paper, a novel fast learning SOM algorithm is proposed. Exploiting a new strategy, the new algorithm runs by concerning "only" about the nodes which are aligned around principal components and neglects the rest of nodes which already include less information [1]. Experimental results are reported at the end of this paper. Two data sets are utilized to illustrate the proposed algorithm. Under same experiment conditions, it is shown here that the computation time is reduced to O(log N) instead of O(N). Also our method computation time is less than that of FDCT by 6 times under same experimental conditions.続きを見る
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Mendeley出力
AlaaPRMU06