Generalized Leverage Scores: Geometric Interpretation and Applications
This work addresses fundamental matrix approximation problems in machine learning, offering incremental improvements in theoretical understanding and algorithm design.
The paper extends leverage scores to relate matrix columns to arbitrary subsets of singular vectors, connecting them to principal angles between subspaces, and applies this to design approximation algorithms for generalized column subset selection and sparse canonical correlation analysis with provable guarantees.
In problems involving matrix computations, the concept of leverage has found a large number of applications. In particular, leverage scores, which relate the columns of a matrix to the subspaces spanned by its leading singular vectors, are helpful in revealing column subsets to approximately factorize a matrix with quality guarantees. As such, they provide a solid foundation for a variety of machine-learning methods. In this paper we extend the definition of leverage scores to relate the columns of a matrix to arbitrary subsets of singular vectors. We establish a precise connection between column and singular-vector subsets, by relating the concepts of leverage scores and principal angles between subspaces. We employ this result to design approximation algorithms with provable guarantees for two well-known problems: generalized column subset selection and sparse canonical correlation analysis. We run numerical experiments to provide further insight on the proposed methods. The novel bounds we derive improve our understanding of fundamental concepts in matrix approximations. In addition, our insights may serve as building blocks for further contributions.