Convex-constrained sparse additive modeling and its extensions

Junming Yin, Yaoliang Yu

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

1 Scopus citations

Abstract

Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. In this work we show how shape constraints such as convexity/ concavity and their extensions, can be integrated into additive models. The proposed sparse difference of convex additive models (SDCAM) can estimate most continuous functions without any a priori smoothness assumption. Motivated by a characterization of difference of convex functions, our method incorporates a natural regularization functional to avoid overfitting and to reduce model complexity. Computationally, we develop an efficient backfitting algorithm with linear periteration complexity. Experiments on both synthetic and real data confirm that our method is competitive against state-of-the-art sparse additive models, with improved performance in most scenarios.

Original languageEnglish (US)
Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 33rd Conference, UAI 2017
PublisherAUAI Press Corvallis
StatePublished - Jan 1 2017
Event33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017 - Sydney, Australia
Duration: Aug 11 2017Aug 15 2017

Other

Other33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017
CountryAustralia
CitySydney
Period8/11/178/15/17

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Yin, J., & Yu, Y. (2017). Convex-constrained sparse additive modeling and its extensions. In Uncertainty in Artificial Intelligence - Proceedings of the 33rd Conference, UAI 2017 AUAI Press Corvallis.