A scalable hybrid KPCA-ELM framework for high-dimensional big data analysis

Abstract

This study aims to address the challenges of analyzing high-dimensional big data by enhancing the Extreme Learning Machine (ELM) with a scalable and efficient feature extraction mechanism. Specifically, we integrate a Scalable Kernel Principal Component Analysis (S-KPCA) into the hidden layer of ELM to improve generalization and computational efficiency. We propose a hybrid framework, termed Fast and Scalable Kernel Principal Component Analysis-Hidden-nodes-based Extreme Learning Machine (FS-KPCA-H-ELM), which embeds KPCA-derived hidden nodes computed via a divide-and-conquer approximation strategy. This approach drastically reduces time complexity from O(n3) to approximately O(n log n), enabling applicability to large-scale datasets. Experiments on 10 big data benchmarks demonstrate that FS-KPCA-H-ELM achieves competitive or superior classification accuracy and substantially reduces training time compared to several existing ELM-based methods. Statistical tests confirm significant improvements over baseline ELM and MapReduce variants, while the method maintains the best average rank among all evaluated approaches. The study presents a novel synergy between KPCA and ELM by replacing random weights with KPCA-derived features, ensuring more informative hidden representations. The scalable approximation ensures feasibility for massive datasets, making the method suitable for real-world big data analytics.