FuSSO: Functional Shrinkage and Selection Operator
Abstract
We present the FuSSO, a functional analogue to the LASSO, that eciently nds a sparse set of functional input covariates to regress a real-valued response against. The FuSSO does so in a semi-parametric fashion, making no parametric assumptions about the nature of input functional covariates and assuming a linear form to the mapping of functional covariates to the response. We provide a statistical backing for use of the FuSSO via proof of asymptotic sparsistency under various conditions. Furthermore, we observe good results on both synthetic and real-world data.
Cite
Text
Oliva et al. "FuSSO: Functional Shrinkage and Selection Operator." International Conference on Artificial Intelligence and Statistics, 2014.Markdown
[Oliva et al. "FuSSO: Functional Shrinkage and Selection Operator." International Conference on Artificial Intelligence and Statistics, 2014.](https://mlanthology.org/aistats/2014/oliva2014aistats-fusso/)BibTeX
@inproceedings{oliva2014aistats-fusso,
title = {{FuSSO: Functional Shrinkage and Selection Operator}},
author = {Oliva, Junier B. and Póczos, Barnabás and Verstynen, Timothy D. and Singh, Aarti and Schneider, Jeff G. and Yeh, Fang-Cheng and Tseng, Wen-Yih Isaac},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2014},
pages = {715-723},
url = {https://mlanthology.org/aistats/2014/oliva2014aistats-fusso/}
}