This paper introduces the Nyström preconditioned conjugate gradient (PCG) algorithm for
solving a symmetric positive-definite linear system. The algorithm applies the randomized …

Linear programming (LP) is an extremely useful tool which has been successfully applied to
solve various problems in a wide range of areas, including operations research …

Kernel discriminant analysis (KDA), the nonlinear extension of linear Discriminant Analysis
(LDA), is a popular tool for learning one or multiple categories in nonlinear data sets …

We present a randomized Kaczmarz method for linear discriminant analysis (rkLDA), an
iterative randomized approach to binary-class Gaussian model linear discriminant analysis …

In statistics and machine learning, logistic regression is a widely-used supervised learning
technique primarily employed for binary classification tasks. When the number of …

Large-sample data became prevalent as data acquisition became cheaper and easier.
While a large sample size has theoretical advantages for many statistical methods, it …

Recent literature has advocated the use of randomized methods foraccelerating the solution
of various matrix problems arising inmachine learning and data science. One popular …

Due to the necessity of electronic transactions with credit cards in this modern era and that
fraudulent activity with credit cards are on the rise, the development of automated systems …

Recently, in statistics and machine learning, the notion of Randomization in Numerical
Linear Algebra (RandNLA) has not only evolved as a vital new tool to design fast and …

In statistics and machine learning, ridge regression (also known as Tikhonov regularization
or weight decay) is a variant of regularized least squares problems where the choice of the …