Feature clustering and mutual information for the selection of variables in spectral data

ESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks

Volumn , Issue , 2007, Pages 157-162

  Krier, C ^a   Francois D ^b   Rossi, F ^c   Verleysen, M ^a  

^a UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

^b CESAME Research Center   (Belgium)

^c INRIA ROCQUENCOURT   (France)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINED CLUSTERING; FEATURE CLUSTERING; FOOD INDUSTRIES; HIGHLY-CORRELATED; MUTUAL INFORMATIONS; SELECTION OF VARIABLES; SPECTRAL VARIABLES; SPECTROSCOPY DATA;

ARTIFICIAL INTELLIGENCE; COGNITIVE SYSTEMS;

NEURAL NETWORKS;

EID: 67650243468     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. F. Rossi, D. François, V. Wertz, M. Verleysen, Fast Selection of Spectral Variables with B-Spline Compression, Chemometrics and Intelligent Laboratory Systems, Elsevier, in press.
2. G. Van Dijk, M.M. Van Hulle, Speeding Up the Wrapper Feature Subset Selection in Regression by Mutual Information Relevance and Redundancy Analysis, International Conference on Artificial Neural Networks 2006, Athens, Greece.
3. C. Krier, D. François, V. Wertz, M. Verleysen, Feature Scoring by Mutual Information for Classification of Mass Spectra, FLINS 2006, 7th International FLINS Conference on Applied Artificial Intelligence, August 29-31, 2006, Genova (Italy).
4. T. Lan, D. Erdogmus, M. Pavel, S. Mathan, Automatic Frequency Bands Segmentation Using Statistical Similarity for Power Spectrum Density Based Brain Computer Interfaces, Proceedings of IJCNN 2006, Vancouver, 2006.
5. Tecator meat sample dataset. Available on Statlib: http://lib.stat.cmu.edu/datasets/tecator.
6. Dataset provided by Prof. Marc Meurens, Université catholique de Louvain, BNUT unit, meurens@bnut.ucl.ac.be.Dataset available from http://www.ucl.ac.be/mlg/.
7. F. Rossi, A. Lendasse, D. Francois, V. Wertz and M. Verleysen, Mutual information for the selection of relevant variables in spectrometric nonlinear modeling, Chemometrics and Intelligent Laboratory Systems 80, pages 215-226, Elsevier, 2006.
8. N. Benoudjit, E. Cools, M. Meurens and M. Verleysen, Chemometric calibration of infrared spectrometers: selection and validation of variables by non-linear models, Chemometrics and Intelligent Laboratory Systems 70, pages 47-53, Elsevier, 2004.
9. N. Benoudjit, Variable selection and neural networks for high-dimensional data analysis, Application in infrared spectroscopy and chemometrics, PhD Thesis, Université Catholique de Louvain, November 2003.