Sparse Additive Model using Symmetric Nonnegative Definite Smoothers
This work addresses the challenge of variable selection and prediction in high-dimensional nonparametric models, representing an incremental improvement over existing sparse backfitting algorithms.
The authors tackled the problem of fitting high-dimensional Sparse Additive Models (SpAM) by introducing an adaptive sparse backfitting algorithm that uses symmetric non-negative definite smoothers, which outperformed previous methods in numerical studies on synthetic and real data.
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.