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ICDM
2009
IEEE

Sparse Least-Squares Methods in the Parallel Machine Learning (PML) Framework

12 years 2 months ago
Sparse Least-Squares Methods in the Parallel Machine Learning (PML) Framework
—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 an empirical loss function, which consists of a 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 parallel implementation in the Parallel Machine Learning (PML) framework, and performance results on the IBM Blue Gene/P parallel computer. Keywords-sparse regression; classification; parallel machine learning;
Ramesh Natarajan, Vikas Sindhwani, Shirish Tatikon
Added 23 May 2010
Updated 23 May 2010
Type Conference
Year 2009
Where ICDM
Authors Ramesh Natarajan, Vikas Sindhwani, Shirish Tatikonda
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