The Pseudo-DF Approach for Learning Huge-scale Data

Abstract

As the advent of the big data era, huge-scale data continuously appears in various fields of science, commerce, industry and society. More algorithms/methods/approaches are urgently required to learn huge-scale data collected from different applications/backgrounds. Therefore, the Pseudo Data Flow (Pseudo-DF) approach with ensemble ReOS-ELMs is proposed in this paper. The Pseudo-DF approach randomly divides a huge-scale data set into K (K>1) non-overlapping data chucks, and a Pseudo-DF is constructed by these data chucks. The computation of a huge-scale data is changed into that of a Pseudo-DF with smaller-scale chucks, the computational burden will be much reduced. Then, the ensemble Regularized OS-ELMs (ReOS-ELMs) based on Different random Hidden-node Parameters (DiffHPs) is presented to learn a Pseudo-DF, which is a recursive leaning algorithm possessing the advantages of low computational burden, high accuracy, well generalization and stability, and strong robustness. Lastly, experiments are performed to validate the effectiveness of the proposed approach.