Topics in Matrix Sampling Algorithms

Understand

We study three fundamental problems of Linear Algebra, lying in the heart of various Machine Learning applications, namely: 1)"Low-rank Column-based Matrix Approximation".

• We are given a matrix A and a target rank k.
• The goal is to select a subset of columns of A and, by using only these columns, compute a rank k approximation to A that is as good as the rank k approximation that would have been obtained by using all the columns; 2) "Coreset Construction in Least-Squares Regression".
• We are given a matrix A and a vector b.