FuSSO: Functional Shrinkage and Selection Operator
Machine Learning
2014-03-11 v2 Machine Learning
Statistics Theory
Statistics Theory
Abstract
We present the FuSSO, a functional analogue to the LASSO, that efficiently finds 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.
Keywords
Cite
@article{arxiv.1311.2234,
title = {FuSSO: Functional Shrinkage and Selection Operator},
author = {Junier B. Oliva and Barnabas Poczos and Timothy Verstynen and Aarti Singh and Jeff Schneider and Fang-Cheng Yeh and Wen-Yih Tseng},
journal= {arXiv preprint arXiv:1311.2234},
year = {2014}
}