Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithms for Big and High-dimensional Data

Oct 11 (Tuesday) at Noon GHC-8102

Speaker: Mladen Kolar

Abstract: ketching techniques have become popular for scaling up machine learning algorithms by reducing the sample size or dimensionality of massive data sets, while still maintaining the statistical power of big data. We study sketching from an optimization point of view. We first show that the iterative Hessian sketch is an optimization process with preconditioning, and develop accelerated iterative Hessian sketch via the searching the conjugate direction; we then establish primal-dual connections between the Hessian sketch and dual random projection, and apply the preconditioned conjugate gradient approach on the dual problem, which leads to the acclerated iterative dual random projection methods. Finally to tackle the challenges from both large sample size and high-dimensionality, we propose the primal-dual sketch, which iteratively sketches the primal and dual formulations. Joint work with Jialei Wang, Jason D. Lee, Mehrdad Mahdavi and Nati Srebro