Linear Projection based on Noise Variance Estimation - Application to Spectral Data

Author

Amaury Lendasse, Missouri University of Science and TechnologyFollow
Francesco Corona

Abstract

In This Paper, We Propose a New Methodology to Build Latent Variables that Are Optimal If a Nonlinear Model is Used afterward. This Method is based on Nonparametric Noise Estimation (Nne). Nne is Providing an Estimate of the Variance of the Noise between Input and Output Variables. the Linear Projection that Builds Latent Variables is Optimized in Order to Minimize the Nne. We Successfully Tested the Proposed Methodology on a Referenced Spectral Dataset from Food Industry (Tecator).

Recommended Citation

A. Lendasse and F. Corona, "Linear Projection based on Noise Variance Estimation - Application to Spectral Data," ESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, pp. 457 - 462, European Symposium on Artificial Neural Networks, Dec 2008.

Department(s)

Engineering Management and Systems Engineering

International Standard Book Number (ISBN)

978-293030708-4

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 European Symposium on Artificial Neural Networks, All rights reserved.

Publication Date

01 Dec 2008