Quantitative Comparison of Linear and Non-linear Dimensionality Reduction Techniques for Solar Image Archives

This work investigates the applicability of several dimensionality reduction techniques for large scale solar data analysis. Using the first solar domain-specific benchmark dataset that contains images of multiple types of phenomena, we investigate linear and non-linear dimensionality reduction methods in order to reduce our storage costs and maintain an accurate representation of our data in a new vector space. We present a comparative analysis between several dimensionality reduction methods and different numbers of target dimensions by utilizing different classifiers in order to determine the percentage of dimensionality reduction that can be achieved on solar data with said methods, and to discover the method that is the most effective for solar images.