Group sparse additive models

Junming Yin, Xi Chen, Eric P. Xing

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

31 Scopus citations


We consider the problem of sparse variable selection in nonparametric additive models, with the prior knowledge of the structure among the covariates to encourage those variables within a group to be selected jointly. Previous works either study the group sparsity in the parametric setting (e.g., group lasso), or address the problem in the nonparametric setting without exploiting the structural information (e.g., sparse additive models). In this paper, we present a new method, called group sparse additive models (GroupSpAM), which can handle group sparsity in additive models. We generalize the ℓ 1/ℓ 2 norm to Hilbert spaces as the sparsity-inducing penalty in GroupSpAM. Moreover, we derive a novel thresholding condition for identifying the functional sparsity at the group level, and propose an efficient block coordinate descent algorithm for constructing the estimate. We demonstrate by simulation that GroupSpAM substantially outperforms the competing methods in terms of support recovery and prediction accuracy in additive models, and also conduct a comparative experiment on a real breast cancer dataset.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th International Conference on Machine Learning, ICML 2012
Number of pages8
StatePublished - 2012
Event29th International Conference on Machine Learning, ICML 2012 - Edinburgh, United Kingdom
Duration: Jun 26 2012Jul 1 2012

Publication series

NameProceedings of the 29th International Conference on Machine Learning, ICML 2012


Other29th International Conference on Machine Learning, ICML 2012
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education


Dive into the research topics of 'Group sparse additive models'. Together they form a unique fingerprint.

Cite this