Some studies on mapping methods

A human being can visualise only up to 3-dimensions. A mapping tool is essential to map the higher dimensional data to a lower dimension for visualisation. Both linear as well as non-linear mapping methods have been used by various researchers for the said purpose. In the present work, a non-linear mapping method based on a genetic algorithm has been developed and its performance is compared to that of other methods, namely Sammon's NLM, VISOR and SOM, in terms of accuracy in mapping, visibility of the mapped data and computational complexity, for solving Schaffer's and DeJong's test functions. The proposed GA-like approach and VISOR algorithm are found to be the best and worst, respectively, in terms of accuracy in mapping, ease of visualization. Moreover, the GA-like approach and VISOR algorithm are seen to be the slowest and fastest, respectively, of all the methods.