Scaled and square-root elastic net

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

Kuvaus

In scaled lasso, the unknown regression coefficients and the scale parameter of the error distribution are estimated jointly. In lasso, the optimal penalty parameter is well-known to depend on the error scale, and it is therefore typically chosen using cross-validation. The main benefit of scaled lasso is that the penalty parameter is scale-free and can be predetermined from pure theoretical considerations. Nevertheless, scaled lasso performs poorly when there exist strong correlations between the predictors. As a remedy, we propose two different scaled elastic net (EN) formulations and derive convergent algorithms for their computation. The first formulation uses a conventional EN penalty whereas the second formulation differs from the former in that the ℓ2-loss is not squared. The former approach is referred to as the scaled EN estimator and the latter as the square-root EN estimator. We illustrate via numerical examples and simulations that the proposed methods outperform the scaled lasso, especially in the presence of high mutual coherence in the feature space.

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
TilaJulkaistu - 16 kesäkuuta 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Acoustics, Speech, and Signal Processing - New Orleans, Yhdysvallat
Kesto: 5 maaliskuuta 20179 maaliskuuta 2017

Julkaisusarja

NimiProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
KustantajaIEEE
ISSN (elektroninen)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
LyhennettäICASSP
MaaYhdysvallat
KaupunkiNew Orleans
Ajanjakso05/03/201709/03/2017

ID: 14451339