Sparse solvers provide essential functionality for a wide variety of scientific applications.
Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi …

Collaborative filtering is a technique that takes into account the common characteristics of
users and items in recommender systems. Matrix decompositions are one of the most used …

This paper presents an adaptive randomized algorithm for computing the butterfly
factorization of an m*n matrix with m≈n provided that both the matrix and its transpose can …

This article presents a low-rank decomposition algorithm based on subsampling of matrix
entries. The proposed algorithm first computes rank-revealing decompositions of …

This article introduces HODLR2D, a new hierarchical low-rank representation for a class of
dense matrices arising out of-body problems in two dimensions. Using this new hierarchical …

The spectrum of a kernel matrix significantly depends on the parameter values of the kernel
function used to define the kernel matrix. This makes it challenging to design a …

In the construction of rank-structured matrix representations of dense kernel matrices, a
heuristic compression method, called the proxy point method, has been used in practice to …

We present a scalable and memory-efficient framework for kernel ridge regression. We
exploit the inherent rank deficiency of the kernel ridge regression matrix by constructing an …

Matrix decompositions play an important role in most of watermarking techniques. Especially
Singular Value Decomposition (SVD) is one of the most preferred techniques. In addition …

Abstract Typically, nonlinear Support Vector Machines (SVMs) produce significantly higher
classification quality when compared to linear ones but, at the same time, their …