USAGE OF DISSIMILARITY MEASURES AND MULTIDIMENSIONAL SCALING FOR LARGE SCALE SOLAR DATA ANALYSIS

Juan M Banda, Rafal Anrgyk

ABSTRACT: This work describes the application of several dissimilarity measures combined with multidimensional scaling for large scale solar data analysis. Using the first solar domain-specific benchmark data set that contains multiple types of phenomena, we investigated combination of different image parameters with different dissimilarity measure sin order to determine which combination will allow us to differentiate our solar data within each class and versus the rest of the classes. In this work we also address the issue of reducing dimensionality by applying multidimensional scaling to our dissimilarity matrices produced by the previously mentioned combination. By applying multidimensional scaling we can investigate how many resulting components are needed in order to maintain a good representation of our data (in an artificial dimensional space) and how many can be discarded in order to economize our storage costs. We present a comparative analysis between different classifiers in order to determine the amount of dimensionality reduction that can be achieved with said combination of image parameters, similarity measure and multidimensional scaling.