Exploiting Local Class Information in Extreme Learning Machine
Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas · 2014
In this paper we propose an algorithm for Single-hidden Layer Feedforward Neural networks training. Based on the observation that the learning process of such networks can be considered to be a non-linear mapping of the training data to a high-dimensional feature space, followed by a data projection process to a low-dimensional space where classification is performed by a linear classifier, we extend the Extreme Learning Machine (ELM) algorithm in order to exploit the local class information in its optimization process. The proposed Local Class Variance Extreme Learning Machine classifier is evaluated in facial image classification problems, where we compare its performance with that of other ELM-based classifiers. Experimental results show that the incorporation of local class information in the ELM optimization process enhances classification performance.