# Online and Adaptive Pseudoinverse Solutions for ELM Weights

arXiv: https://arxiv.org/abs/1405.7777 · HTML: https://mlsanity.com/paper/1405.7777/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2014.

## Built on by (most cited first, 3 of 3)

- [A contextual analysis of multi-layer perceptron models in classifying hand-written digits and letters: limited resources](https://mlsanity.com/paper/2107.01782/) (2021)
- [KCNet: An Insect-Inspired Single-Hidden-Layer Neural Network with Randomized Binary Weights for Prediction and Classification Tasks](https://mlsanity.com/paper/2108.07554/) (2021)
- [Machine learning based lens-free imaging technique for field-portable cytometry](https://mlsanity.com/paper/2203.00899/) (2022)

## Abstract (arXiv)

The ELM method has become widely used for classification and regressions problems as a result of its accuracy, simplicity and ease of use. The solution of the hidden layer weights by means of a matrix pseudoinverse operation is a significant contributor to the utility of the method; however, the conventional calculation of the pseudoinverse by means of a singular value decomposition (SVD) is not always practical for large data sets or for online updates to the solution. In this paper we discuss incremental methods for solving the pseudoinverse which are suitable for ELM. We show that careful choice of methods allows us to optimize for accuracy, ease of computation, or adaptability of the solution.