FuSSO: Functional Shrinkage and Selection Operator

Junier Oliva, Barnabas Poczos, Timothy Verstynen, Aarti Singh, Jeff Schneider, Fang-Cheng Yeh, Wen-Yih Tseng
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:715-723, 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v33-oliva14b, title = {{FuSSO: Functional Shrinkage and Selection Operator}}, author = {Oliva, Junier and Poczos, Barnabas and Verstynen, Timothy and Singh, Aarti and Schneider, Jeff and Yeh, Fang-Cheng and Tseng, Wen-Yih}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {715--723}, year = {2014}, editor = {Kaski, Samuel and Corander, Jukka}, volume = {33}, series = {Proceedings of Machine Learning Research}, address = {Reykjavik, Iceland}, month = {22--25 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v33/oliva14b.pdf}, url = {https://proceedings.mlr.press/v33/oliva14b.html}, 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.} }
Endnote
%0 Conference Paper %T FuSSO: Functional Shrinkage and Selection Operator %A Junier Oliva %A Barnabas Poczos %A Timothy Verstynen %A Aarti Singh %A Jeff Schneider %A Fang-Cheng Yeh %A Wen-Yih Tseng %B Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2014 %E Samuel Kaski %E Jukka Corander %F pmlr-v33-oliva14b %I PMLR %P 715--723 %U https://proceedings.mlr.press/v33/oliva14b.html %V 33 %X 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.
RIS
TY - CPAPER TI - FuSSO: Functional Shrinkage and Selection Operator AU - Junier Oliva AU - Barnabas Poczos AU - Timothy Verstynen AU - Aarti Singh AU - Jeff Schneider AU - Fang-Cheng Yeh AU - Wen-Yih Tseng BT - Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-oliva14b PB - PMLR DP - Proceedings of Machine Learning Research VL - 33 SP - 715 EP - 723 L1 - http://proceedings.mlr.press/v33/oliva14b.pdf UR - https://proceedings.mlr.press/v33/oliva14b.html AB - 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. ER -
APA
Oliva, J., Poczos, B., Verstynen, T., Singh, A., Schneider, J., Yeh, F. & Tseng, W.. (2014). FuSSO: Functional Shrinkage and Selection Operator. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 33:715-723 Available from https://proceedings.mlr.press/v33/oliva14b.html.

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