Accelerated Nonlinear Gaussianization for Feature Extraction

Alexandru Paul Condurache, Alfred Mertins

2013

Abstract

In a multi-class classification setup, the Gaussianization represents a nonlinear feature extraction transform with the purpose of achieving Gaussian class-conditional densities in the transformed space. The computational complexity of such a transformation increases with the dimension of the processed feature space in such a way that only relatively small dimensions can be processed. In this contribution we describe how to reduce the computational burden with the help of an adaptive grid. Thus, the Gaussianization transform is able to also handle feature spaces of higher dimensionality, improving upon its practical usability. On both artificially generated and real-application data, we demonstrate a decrease in computation complexity in comparison to the standard Gaussianization, while maintaining the effectiveness.

Paper Citation

in Harvard Style

Paul Condurache A. and Mertins A. (2013). Accelerated Nonlinear Gaussianization for Feature Extraction . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 121-126. DOI: 10.5220/0004204701210126

in Bibtex Style

@conference{icpram13,
author={Alexandru Paul Condurache and Alfred Mertins},
title={Accelerated Nonlinear Gaussianization for Feature Extraction},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={121-126},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004204701210126},
isbn={978-989-8565-41-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Accelerated Nonlinear Gaussianization for Feature Extraction
SN - 978-989-8565-41-9
AU - Paul Condurache A.
AU - Mertins A.
PY - 2013
SP - 121
EP - 126
DO - 10.5220/0004204701210126