Abstract
Variable Selection is a Crucial Part of Building Regression Models and is Preferably Done as a Filtering Method Independently from the Model Training. Mutual Information is a Popular Relevance Criterion for This, But It is Not Trivial to Estimate Accurately from a Limited Amount of Data. in This Paper, a Method is Presented Where a Gaussian Mixture Model is Used to Estimate the Joint Density of the Input and Output Variables, and Subsequently Used to Select the Most Relevant Variables by Maximizing the Mutual Information Which Can Be Estimated using the Model.
Recommended Citation
E. Eirola et al., "Variable Selection for Regression Problems using Gaussian Mixture Models to Estimate Mutual Information," Proceedings of the International Joint Conference on Neural Networks, pp. 1606 - 1613, article no. 6889561, Institute of Electrical and Electronics Engineers, Sep 2014.
The definitive version is available at https://doi.org/10.1109/IJCNN.2014.6889561
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-147991484-5
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
03 Sep 2014
