LOCO: Distributing Ridge Regression with Random Projections

Abstract

We propose LOCO, an algorithm for large-scale ridge regression which distributes the features across workers on a cluster. Important dependencies between variables are preserved using structured random projections which are cheap to compute and must only be communicated once. We show that LOCO obtains a solution which is close to the exact ridge regression solution in the fixed design setting. We verify this experimentally in a simulation study as well as an application to climate prediction. Furthermore, we show that LOCO achieves significant speedups compared with a state-of-the-art distributed algorithm on a large-scale regression problem.

37 pages

References in corpus (1)

• Parallel Coordinate Descent for L1-Regularized Loss Minimization

Cited by in corpus (2)

• Adding vs. Averaging in Distributed Primal-Dual Optimization
• Stochastic, Distributed and Federated Optimization for Machine Learning