OS-λ1-ELM: Online sequential λ1-regularized-ELM based on ADMM

摘要

As business data and scientific data become larger and larger, the study of incremental learning algorithms becomes more and more important. Online sequential extreme learning machine (OS-ELM) algorithm is an incremental learning algorithm that can learn data one by one. On the basis of OS-ELM, an online sequential extreme learning machine incremental learning algorithm is proposed based on the λ-regularization (OS-λ-ELM). The proposed method can make use of the original learning results and does not need to re-learn all the data, thus can save time and space resources. By adding λ-regularization, the sparse model can effectively avoid the over-fitting problem. At the same time, alternating direction method of multipliers (ADMM) and the proximity algorithm are used to solve the OS-λ-ELM. The algorithm is deduced into a recursive form, which greatly reduces the computational complexity. Experimental results show that the proposed method has good generalization and robustness.