Stochastic Optimization of PCA with Capped MSG

Principal Component Analysis (PCA) is a ubiquitous tool used in many data analysis, machine learning and information retrieval applications. It is used for obtaining a lower dimensional representation of a high dimensional signal that still captures as much as possible of the original signal. Such a low dimensional representation can be useful for reducing storage and computational costs, as complexity control in learning systems, or to aid in visualization.