Subspace embeddings for the polynomial kernel
Haim Avron, Huy L. Nguyễn, et al.
NeurIPS 2014
We study the following problem of subset selection for matrices: given a matrix X ε ℝnxm (m > n) and a sampling parameter k (n ≤ k ≤ m), select a subset of k columns from X such that the pseudoinverse of the sampled matrix has as small a norm as possible. In this work, we focus on the Frobenius and the spectral matrix norms. We describe several novel (deterministic and randomized) approximation algorithms for this problem with approximation bounds that are optimal up to constant factors. Additionally, we show that the combinatorial problem of finding a low-stretch spanning tree in an undirected graph corresponds to subset selection, and discuss various implications of this reduction. © by SIAM.
Haim Avron, Huy L. Nguyễn, et al.
NeurIPS 2014
Osman Asif Malik, Shashanka Ubaru, et al.
SDM 2021
Gal Shulkind, Lior Horesh, et al.
SIAM/ASA JUQ
Christos Boutsidis, Petros Drineas, et al.
NeurIPS 2011