GURLS: a Least Squares Library for Supervised Learning

TL;DR

GURLS is a modular, efficient software library for supervised learning using least squares, supporting large-scale, multi-output problems with advanced model selection and distributed computation capabilities.

Contribution

It introduces a flexible, easy-to-extend library for supervised learning that leverages regularized least squares and supports large-scale and multi-output problems in MATLAB and C++.

Findings

Supports large matrices with memory-mapped storage

Provides state-of-the-art training strategies

Enables efficient model selection and distributed computation

Abstract

We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised learning. GURLS is targeted to machine learning practitioners, as well as non-specialists. It offers a number state-of-the-art training strategies for medium and large-scale learning, and routines for efficient model selection. The library is particularly well suited for multi-output problems (multi-category/multi-label). GURLS is currently available in two independent implementations: Matlab and C++. It takes advantage of the favorable properties of regularized least squares algorithm to exploit advanced tools in linear algebra. Routines to handle computations with very large matrices by means of memory-mapped storage and distributed task execution are available. The package is distributed under the BSD licence and is available for download at https://github.com/CBCL/GURLS.