# Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics

arXiv: https://arxiv.org/abs/2205.13191 · HTML: https://mlsanity.com/paper/2205.13191/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 3 of 3)

- [A Survey on Large-scale Machine Learning](https://mlsanity.com/paper/2008.03911/) (2020)
- [Stochastic Configuration Networks: Fundamentals and Algorithms](https://mlsanity.com/paper/1702.03180/) (2017)
- [Constructive neural network learning](https://mlsanity.com/paper/1605.00079/) (2016)

## Built on by (most cited first, 1 of 1)

- [Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint](https://mlsanity.com/paper/2212.09062/) (2022)

## Abstract (arXiv)

As a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning. However, the randomness makes SCNs more likely to generate approximate linear correlative nodes that are redundant and low quality, thereby resulting in non-compact network structure. In the light of a fundamental principle in machine learning, that is, a model with fewer parameters holds improved generalization. This paper proposes orthogonal SCN, termed OSCN, to filtrate out the low-quality hidden nodes for network structure reduction by incorporating Gram-Schmidt orthogonalization technology. The universal approximation property of OSCN and an adaptive setting for the key construction parameters have been presented in details. In addition, an incremental updating scheme is developed to dynamically determine the output weights, contributing to improved computational efficiency. Finally, experimental results on two numerical examples and several real-world regression and classification datasets substantiate the effectiveness and feasibility of the proposed approach.