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

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).

• Type: Conference
• Year: 2008
• Where: ESANN
• Authors: Amaury Lendasse, Francesco Corona
• Keywords: ESANN 2008, Latent Variables, Neural Networks, Nonparametric Noise Estimation, Referenced Spectral Dataset