Minimum Variance Extreme Learning Machine for human action recognition

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas
2014 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
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 training data dispersion
more » ... g data dispersion in its optimization process. The proposed Minimum Variance Extreme Learning Machine classifier is evaluated in human action recognition, where we compare its performance with that of other ELM-based classifiers, as well as the kernel Support Vector Machine classifier.
doi:10.1109/icassp.2014.6854640 dblp:conf/icassp/IosifidisTP14 fatcat:wx34ikawmzcixmekhy7vrvkwnq