A-ELM⁎: Adaptive Distributed Extreme Learning Machine with MapReduce

Due to the outstanding advantage, such as generalization performance and fast convergence, Extreme Learning Machine (ELM) and its variants have been widely used for many applications. The distributed ELM with MapReduce could handle large-scale training dataset efficiently, but how to cope with its updated hidden nodes number which aims to get the higher accuracy is still a challenging task. In this paper, we propose a novel Adaptive Distributed Extreme Learning Machine with MapReduce (A-ELM⁎). It could overcome the weakness of ELM⁎ in learning massive training dataset for updating hidden nodes number. Firstly, we found that through partial adjustment of incremental hidden nodes and decremental hidden nodes, matrix multiplication (the most computation-expensive part in A-ELM⁎) can be calculated. Next, A-ELM⁎ based on MapReduce framework is proposed. A-ELM⁎ first calculates the intermediate matrix multiplications of the updated hidden nodes subset, and then update the matrix multiplications by modifying the old matrix multiplications with the intermediate ones. Then, based on the updated matrix multiplications, there could obtain the corresponding new output weight vector with centralized computing. Therefore, it is effective for learning large scale training dataset, in which the hidden nodes update rapidly. Finally, we verify the effectiveness and efficiency of our proposed A-ELM⁎, using synthetic data with extensive experiments, in learning updated hidden nodes.