2017/05/01 by Junming Yin, Yin, Junming, Yaoliang Yu +1
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Graph Theory and Algorithms #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Manufacturing Process and Optimization #Medical Image Segmentation Techniques #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1705.00687
17 pages, 2 figures
arxiv created 2017/05/01 · openalex publication_date 2017/05/01 · arxiv updated 2017/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 per-iteration complexity. Experiments on both synthetic and real data verify that our method is competitive against state-of-the-art sparse additive models, with improved performance in most scenarios.