Sparse Group Lasso for regression on land climate variables

Soumyadeep Chatterjee, Arindam Banerjee, Singdhansu B Chatterjee, Auroop R. Ganguly

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The large amount of reliable climate data available today has promoted the development of statistical predictive models for climate variables. In this paper we have applied Sparse Group Lasso to build a predictive model for land climate variables using ocean climate variables as covariates.We demonstrate that the sparse model provides better predictive performance than the state-of-the-art, is climatologically interpretable and robust in variable selection.

Original languageEnglish (US)
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Pages1-8
Number of pages8
DOIs
StatePublished - Dec 1 2011
Event11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 11 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
CountryCanada
CityVancouver, BC
Period12/11/1112/11/11

Keywords

  • Climate prediction
  • Sparse Group Lasso
  • Sparse regression

Cite this

Chatterjee, S., Banerjee, A., Chatterjee, S. B., & Ganguly, A. R. (2011). Sparse Group Lasso for regression on land climate variables. In Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 (pp. 1-8). [6137353] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDMW.2011.155