A Novel Relaxed ADMM with Highly Parallel Implementation for Extreme Learning Machine

摘要

One of the most attractive features of the extreme learning machine (ELM) is its fast speed of learning. In a big data environment, however, ELM may still suffer an overly-heavy computational issue. This paper presents a novel relaxed alternating direction method of multipliers (ADMM) for convex model fitting problems with a focus on a highly parallel implementation for least-squares problems arising from neural network training by ELM. Convergence results and computational complexity of the relaxed ADMM for least-squares problems are given, and comparisons with existing methods are also provided.