A Fast Greedy Algorithm for Generalized Column Subset Selection
This work addresses a computational bottleneck in matrix approximation for researchers and practitioners in machine learning and data analysis, but it is incremental as it builds on existing subset selection methods.
The paper tackles the generalized column subset selection problem, which involves selecting columns from a source matrix to approximate a target matrix's span, and proposes a fast greedy algorithm that efficiently solves this and related problems.
This paper defines a generalized column subset selection problem which is concerned with the selection of a few columns from a source matrix A that best approximate the span of a target matrix B. The paper then proposes a fast greedy algorithm for solving this problem and draws connections to different problems that can be efficiently solved using the proposed algorithm.