A Novel Relaxed ADMM with Highly Parallel Implementation for Extreme Learning Machine
Xiaoping Lai, Jiuwen Cao, Zhiping Lin · 2018
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.