Matrix sampling algorithms for topics in machine learning

Saurabh Paul

Abstract: We study the application of matrix sampling algorithms on four problems in machine learning, namely: (i) Feature Selection for Linear Support Vector Machines, (ii) Feature Selection for Ridge Regression, (iii) Core-set Construction for Canonical Correlation Analysis and (iv) Adaptive Sampling algorithm for matrix reconstruction. We provide both theoretical performance guarantees and empirical evidence to indicate the effectiveness of our methods. A more detailed description is given below.