An Efficient Initialization Scheme For Som Algorithm Based On Reference Point And Filters

摘要

This paper proposes an efficient self-organizing map algorithm based on reference point and niters. A strategy called Reference Point SOM (RPSOM) is proposed to improve SOM execution time by means of filtering with two thresholds T_1 and T_2. We use one threshold, T_1, to define the search boundary parameter used to search for the Best-Matching Unit (BMU) with respect to input vectors. The other threshold, T_2, is used as the search boundary within which the BMU finds its neighbors. The proposed algorithm reduces the time complexity from O(n~2) to O(ri) in finding the initial neurons as compared to the algorithm proposed by Su et al. 16. The RPSOM dramatically reduces the time complexity, especially in the computation of large data set. From the experimental results, we find that it is better to construct a good initial map and then to use the unsupervised learning to make small subsequent adjustments.