Impact of Data Dimensionality Reduction on Neural Based Classification: Application to Industrial Defects

Matthieu Voiry, Kurosh Madani, Véronique Amarger, Joël Bernier

Abstract

A major step for high-quality optical surfaces faults diagnosis concerns scratches and digs defects characterisation. This challenging operation is very important since it is directly linked with the produced optical component’s quality. To complete optical devices diagnosis, a classification phase is mandatory since a number of correctable defects are usually present beside the potential “abiding” ones. Unfortunately relevant data extracted from raw image during defects detection phase are high dimensional. This can have harmful effect on behaviors of artificial neural networks which are suitable to perform such a challenging classification. Reducing data dimension to a smaller value can however decrease problems related to high dimensionality. In this paper we compare different techniques which permit dimensionality reduction and evaluate their possible impact on classification tasks performances.

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Paper Citation


in Harvard Style

Voiry M., Madani K., Amarger V. and Bernier J. (2007). Impact of Data Dimensionality Reduction on Neural Based Classification: Application to Industrial Defects . In Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2007) ISBN 978-972-8865-86-3, pages 56-65. DOI: 10.5220/0001635500560065


in Bibtex Style

@conference{anniip07,
author={Matthieu Voiry and Kurosh Madani and Véronique Amarger and Joël Bernier},
title={Impact of Data Dimensionality Reduction on Neural Based Classification: Application to Industrial Defects},
booktitle={Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2007)},
year={2007},
pages={56-65},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001635500560065},
isbn={978-972-8865-86-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2007)
TI - Impact of Data Dimensionality Reduction on Neural Based Classification: Application to Industrial Defects
SN - 978-972-8865-86-3
AU - Voiry M.
AU - Madani K.
AU - Amarger V.
AU - Bernier J.
PY - 2007
SP - 56
EP - 65
DO - 10.5220/0001635500560065