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

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3 Author(s)
Natarajan, R. ; IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA ; Sindhwani, V. ; Tatikonda, S.

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 Lenclos-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.

Published in:

Data Mining Workshops, 2009. ICDMW '09. IEEE International Conference on

Date of Conference:

6-6 Dec. 2009