Signal Self Organizing Map

The self organizing map (SOM) has been applied to wide ranges of fields including computer vision and image processing. Despite of its simple training algorithm, the vectorial input pattern of SOMs induced a sequence of drawbacks which should not be overlooked. These drawbacks include optimal description length selection problem and inaccurate clustering of scattered point patterns. In this article, an extension of SOM to continuous domain, namely signal SOM (SSOM), is proposed to tackle the drawbacks caused by the vectorial input pattern SOMs. Remarkably, it provides an analytical model expression and involves no model selection problem. The SSOM is evaluated by a simulation about clustering of three signal groups. By comparing with the conventional SOM, a more structural map in term of signal group distribution is obtained by the SSOM. Thus, it indicate the contribution of this article on extending the ability of SOM.