FuSSO: Functional Shrinkage and Selection Operator - Robotics Institute Carnegie Mellon University

FuSSO: Functional Shrinkage and Selection Operator

J. Oliva, B. Poczos, T. Verstynen, A. Singh, J. Schneider, F. Yeh, and W. Tseng
Conference Paper, Proceedings of 17th International Conference on Artificial Intelligence and Statistics (AISTATS '14), Vol. 33, pp. 715 - 723, April, 2014

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.

BibTeX

@conference{Oliva-2014-119783,
author = {J. Oliva and B. Poczos and T. Verstynen and A. Singh and J. Schneider and F. Yeh and W. Tseng},
title = {FuSSO: Functional Shrinkage and Selection Operator},
booktitle = {Proceedings of 17th International Conference on Artificial Intelligence and Statistics (AISTATS '14)},
year = {2014},
month = {April},
volume = {33},
pages = {715 - 723},
}