Generalized Additive Model Selection
This work addresses model selection for high-dimensional data in statistics and machine learning, but it appears incremental as it builds on existing penalized likelihood and additive model frameworks.
The authors tackled the problem of fitting sparse generalized additive models in high dimensions by introducing GAMSEL, a penalized likelihood method that selects variable effects as zero, linear, or low-complexity curves, and demonstrated its performance on real and simulated data with comparisons to existing techniques.
We introduce GAMSEL (Generalized Additive Model Selection), a penalized likelihood approach for fitting sparse generalized additive models in high dimension. Our method interpolates between null, linear and additive models by allowing the effect of each variable to be estimated as being either zero, linear, or a low-complexity curve, as determined by the data. We present a blockwise coordinate descent procedure for efficiently optimizing the penalized likelihood objective over a dense grid of the tuning parameter, producing a regularization path of additive models. We demonstrate the performance of our method on both real and simulated data examples, and compare it with existing techniques for additive model selection.