Sparse Additive Model using Symmetric Nonnegative Definite Smoothers
Abstract
We introduce a new algorithm, called adaptive sparse backfitting algorithm, for solving high dimensional Sparse Additive Model (SpAM) utilizing symmetric, non-negative definite smoothers. Unlike the previous sparse backfitting algorithm, our method is essentially a block coordinate descent algorithm that guarantees to converge to the optimal solution. It bridges the gap between the population backfitting algorithm and that of the data version. We also prove variable selection consistency under suitable conditions. Numerical studies on both synthesis and real data are conducted to show that adaptive sparse backfitting algorithm outperforms previous sparse backfitting algorithm in fitting and predicting high dimensional nonparametric models.
Cite
@article{arxiv.1409.2552,
title = {Sparse Additive Model using Symmetric Nonnegative Definite Smoothers},
author = {Yan Li},
journal= {arXiv preprint arXiv:1409.2552},
year = {2014}
}
Comments
This is a term project report and has been withdrawn by the authors; arXiv admin note: author list has been modified due to misrepresentation of authorship