Self Organizing Maps with Delay Actualization

The paper deals with the Self Organizing Maps (SOM). The SOM is a standard tool for clustering and visualization of high-dimensional data. The learning phase of SOM is time-consuming especially for large datasets. There are two main bottleneck in the learning phase of SOM: finding of a winner of competitive learning process and updating of neurons’ weights. The paper is focused on the second problem. There are two extremal update strategies. Using the first strategy, all necessary updates are done immediately after processing one input vector. The other extremal choice is used in Batch SOM – updates are processed at the end of whole epoch. In this paper we study update strategies between these two extremal strategies. Learning of the SOM with delay updates are proposed in the paper. Proposed strategies are also experimentally evaluated.