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

Our motivation believe our results will be of special interest to researchers for this work comes from the fact that with the large from the field of medical image analysis, as these images amounts of data that the SDO mission started transmitting, seem to be the closest to our dataset [21. We also identify hand labeling (commonly used by solar physicist in the last some interesting combinations of dimensionality reduction decades) of these images is simply impossible. There have methods and classifiers that behave differently across the been several successful CBIR systems for medical images presented datasets. Our research problem in Solar physics is [2] as well as in other domains [3]; none of them, however, of great practical relevance for Earth's climate since solar have dealt with the volume of data that the SDO mission flares endanger the lives of passengers on commercial airline generates. This NASA mission, only with its Atmospheric routes going over the poles, interrupt radio communications Imaging Assembly (AIA), generates eight 4096 pixels x in bands the military uses, can (and have) knocked 4096 pixels images every 10 seconds. This leads to a data down power grids. The systematic feature recognition and transmission rate of approximately 700 gigabytes per day the study of the metadata, is a key component of the ultimate only from the AIA component (the entire mission is expected prediction of solar activity (space weather).