Date of Original Version

4-2014

Type

Conference Proceeding

Rights Management

Copyright 2014 by the authors.

Abstract or Description

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.

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Published In

Journal of Machine Learning Research : Workshop and Conference Proceedings, 33, 715-723.