Rank-Revealing Orthogonal Decomposition in Extreme Learning Machine Design

摘要

Extreme Learning Machine (ELM), a neural network technique used for regression problems, may be considered as a nonlinear transformation (from the training input domain into the output space of hidden neurons) which provides the basis for linear mean square (LMS) regression problem. The conditioning of this problem is the important factor influencing ELM implementation and accuracy. It is demonstrated that rank-revealing orthogonal decomposition techniques can be used to identify neurons causing collinearity among LMS regression basis. Such neurons may be eliminated or modified to increase the numerical rank of the matrix which is pseudo-inverted while solving LMS regression.