Compressive Statistical Learning with Random Feature Moments

Large-scale machine learning faces a number of fundamental computational challenges, triggered both by the high dimensionality of modern data and the increasing availability of very large training collections. Besides the need to cope with high-dimensional features extracted from images, volumetric data, etc., a key challenge is to develop techniques able to fully leverage the information content and learning opportunities opened by large training collections of millions to billions or more items, with controlled computational resources. Such training volumes can severely challenge traditional statistical learning paradigms based on batch empirical risk minimization.