Nonparametric reduced rank regression

Rina Foygel, Michael Horrell, Mathias Drton, John Lafferty

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

10 Zitate (Scopus)

Abstract

We propose an approach to multivariate nonparametric regression that generalizes reduced rank regression for linear models. An additive model is estimated for each dimension of a q-dimensional response, with a shared p-dimensional predictor variable. To control the complexity of the model, we employ a functional form of the Ky-Fan or nuclear norm, resulting in a set of function estimates that have low rank. Backfitting algorithms are derived and justified using a nonparametric form of the nuclear norm subdifferential. Oracle inequalities on excess risk are derived that exhibit the scaling behavior of the procedure in the high dimensional setting. The methods are illustrated on gene expression data.

OriginalspracheEnglisch
TitelAdvances in Neural Information Processing Systems 25
Untertitel26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012
Seiten1628-1636
Seitenumfang9
PublikationsstatusVeröffentlicht - 2012
Extern publiziertJa
Veranstaltung26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 - Lake Tahoe, NV, USA/Vereinigte Staaten
Dauer: 3 Dez. 20126 Dez. 2012

Publikationsreihe

NameAdvances in Neural Information Processing Systems
Band2
ISSN (Print)1049-5258

Konferenz

Konferenz26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012
Land/GebietUSA/Vereinigte Staaten
OrtLake Tahoe, NV
Zeitraum3/12/126/12/12

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