Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics

Understand

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.