Optimization of regularization coefficient and kernel parameters of KELM in face recognition using genetic algorithm

Extreme Learning machine (ELM), a linear system model introduced originally for feedforward neural networks having single-hidden layer, which is non-iteratively tuned. In ELM feature mapping is achieved explicitly with the help of activation functions. ELM was further prospected with kernel functions for performance improvement by exploiting implicit feature mapping. The classification accuracy of kernel ELM relies on the kernel and structural parameters, which are experimentally tuned. It is very challenging and computationally hard to assess the optimal choice of these parameters from a specific domain of values using Brute force method. Therefore an optimal algorithm is required to find the finest combination of these parameters for enhanced performance. In this paper optimized kernel ELM is unveiled, in which genetic algorithm is utilized to evaluate the optimal solution of regularization coefficient and kernel parameters. Experiments on face image databases denote that the developed algorithm is more accurate and efficacious than state of art existing algorithms in literature.

Automated summary

This paper introduces kernel ELM, evaluates the optimal solution using genetic algorithm, and improves performance, with experimental results showing it to be more accurate and efficient than existing algorithms in the literature.