Classification of Big Data with Application to Imaging Genetics

ECENT technological achievements and globalization have increased data acquisition capability in almost all corners of human activities, ranging from scientific and engineering endeavors such as genomics, medical imaging, remote sensing, economics and finance, and all the way to people's personal lives with the emergence of social media through the world wide web and mobile networks. The enormous growth of data creates daunting challenges, not only in finding out how to store and access the data, but more importantly, how to process and make sense of it. Also, since data collection is expensive, we are somehow obliged to make good use of the data at hand, so it is obvious that for further progress, the development of efficient algorithms for processing big data is very important. Big data is usually considered in terms of the number of observations n and the number of variables p measured on each observation. In many branches of science such as genetics and medical imaging, the number of variables is very large and is often much larger than the number of observations. This scenario is often denoted as p n.