  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.
