Abstract:Abstract-We describe parallel methods for solving large-scale, high-dimensional, sparse least-squares problems that arise in machine learning applications such as document classification. The basic idea is to solve a two-class response problem using a fast regression technique based on minimizing a loss function, which consists of an empirical squared-error term, and one or more regularization terms. We consider the use of Lanczos-based methods for solving these regularized least-squares problems, with the par… Show more
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