Rank Based Pseudoinverse Computation in Extreme Learning Machine for Large Datasets

TL;DR

This paper introduces a rank-based pseudoinverse computation method for Extreme Learning Machines to significantly reduce training time and computational complexity when handling large datasets with many hidden nodes.

Contribution

A novel rank-based matrix decomposition approach for ELM that achieves near-minimal training time for large-scale problems, improving over existing methods like DF-ELM.

Findings

Constant training time close to minimal

Significant reduction in computational complexity

Outperforms existing methods in large datasets

Abstract

Extreme Learning Machine (ELM) is an efficient and effective least-square-based learning algorithm for classification, regression problems based on single hidden layer feed-forward neural network (SLFN). It has been shown in the literature that it has faster convergence and good generalization ability for moderate datasets. But, there is great deal of challenge involved in computing the pseudoinverse when there are large numbers of hidden nodes or for large number of instances to train complex pattern recognition problems. To address this problem, a few approaches such as EM-ELM, DF-ELM have been proposed in the literature. In this paper, a new rank-based matrix decomposition of the hidden layer matrix is introduced to have the optimal training time and reduce the computational complexity for a large number of hidden nodes in the hidden layer. The results show that it has constant training time which is closer towards the minimal training time and very far from worst-case training time of the DF-ELM algorithm that has been shown efficient in the recent literature.