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Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models

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Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models

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Laria, JC.; Aguilera-Morillo, MC.; Lillo, RE. (2022). Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models. Statistical Papers. 64(1):227-253. https://doi.org/10.1007/s00362-022-01313-z

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Título: Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models
Autor: Laria, Juan C. Aguilera-Morillo, M. Carmen Lillo, Rosa E.
Entidad UPV: Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials
Fecha difusión:
Resumen:
[EN] This paper introduces the Group Linear Algorithm with Sparse Principal decomposition, an algorithm for supervised variable selection and clustering. Our approach extends the Sparse Group Lasso regularization to calculate ...[+]
Palabras clave: Regression , Classification , Feature clustering , Statistical computing
Derechos de uso: Reserva de todos los derechos
Fuente:
Statistical Papers. (issn: 0932-5026 )
DOI: 10.1007/s00362-022-01313-z
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/s00362-022-01313-z
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104901RB-I00/ES/NUEVAS ESTRATEGIAS EN REGRESION PENALIZADA CON APLICACIONES EN SALUD, DEMOGRAFIA Y ECONOMIA/
Tipo: Artículo

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