Parsimonious Wavelet Kernel Extreme Learning Machine

Abstract

In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM) was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM). In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM) maximized its capability to capture the essential features in "frequency-rich" signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

Mexican Hat Wavelet Kernel ELM for Multiclass Classification

“…Although [11] solves the problem of dead nodes and dimension explosion in a sense, the performance of the traditional kernel function for multiclass classification problems is still not very good. From [12, 13], we know that wavelet functions can be used in SVM and ELM, which have a strong fitting capability. Therefore, in this paper, we propose a Mexican Hat wavelet kernel ELM (MHW-KELM) classifier, which effectively solves the problems in the conventional classifier.…”