Search results for "Group lasso"

showing 3 items of 3 documents

Propagation pattern analysis during atrial fibrillation based on the adaptive group LASSO.

2012

The present study introduces sparse modeling for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence (PDC) function, derived from fitting a multivariate autoregressive model to the observed signals. A sparse optimization method is proposed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO). In simulations aLASSO was found superior to the commonly used least-squares (LS) estimation with respect to estimation performance. The normalized error between the true and estimated model parameters dropped from 0.200.04 for LS estimatio…

Normalization (statistics)Computer scienceBiomedical EngineeringHealth InformaticsGroup lassoSensitivity and SpecificityPattern Recognition AutomatedHeart Conduction SystemStatisticsAtrial FibrillationCoherence (signal processing)AnimalsHumansComputer SimulationDiagnosis Computer-AssistedTime series1707ShrinkageSparse matrixPropagation patternModels CardiovascularReproducibility of ResultsElectroencephalographySignal ProcessingSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaAlgorithmAlgorithmsAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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An efficient algorithm to estimate the sparse group structure of an high-dimensional generalized linear model

2014

Massive regression is one of the new frontiers of computational statistics. In this paper we propose a generalization of the group least angle regression method based on the differential geometrical structure of a generalized linear model specified by a fixed and known group structure of the predictors. An efficient algorithm is also proposed to compute the proposed solution curve.

Group lassoGeneralized linear modelDifferential geometrySettore SECS-S/01 - Statisticadglar
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A differential-geometric approach to generalized linear models with grouped predictors

2016

We propose an extension of the differential-geometric least angle regression method to perform sparse group inference in a generalized linear model. An efficient algorithm is proposed to compute the solution curve. The proposed group differential-geometric least angle regression method has important properties that distinguish it from the group lasso. First, its solution curve is based on the invariance properties of a generalized linear model. Second, it adds groups of variables based on a group equiangularity condition, which is shown to be related to score statistics. An adaptive version, which includes weights based on the Kullback-Leibler divergence, improves its variable selection fea…

Statistics and ProbabilityGeneralized linear modelStatistics::TheoryMathematical optimizationProper linear modelGeneral MathematicsORACLE PROPERTIESGeneralized linear modelSPARSITYGeneralized linear array model01 natural sciencesGeneralized linear mixed modelCONSISTENCY010104 statistics & probabilityScore statistic.LEAST ANGLE REGRESSIONLinear regressionESTIMATORApplied mathematicsDifferential geometry0101 mathematicsDivergence (statistics)MathematicsVariance functionDifferential-geometric least angle regressionPATH ALGORITHMApplied MathematicsLeast-angle regressionScore statistic010102 general mathematicsAgricultural and Biological Sciences (miscellaneous)Group lassoGROUP SELECTIONStatistics Probability and UncertaintyGeneral Agricultural and Biological SciencesSettore SECS-S/01 - Statistica
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