Two Novel Methods For The Determination Of The Number Of Components In Independent Components Analysis Models

Jouan-Rimbaud Bouveresse, D.; Moya Gonzalez, Adolfo; Ammari, F. y Rutledge, Douglas N. (2012). Two Novel Methods For The Determination Of The Number Of Components In Independent Components Analysis Models. "Chemometrics And Intelligent Laboratory Systems", v. 112 ; pp. 24-32. ISSN 0169-7439. https://doi.org/10.1016/j.chemolab.2011.12.005.

Descripción

Título: Two Novel Methods For The Determination Of The Number Of Components In Independent Components Analysis Models
Autor/es:
  • Jouan-Rimbaud Bouveresse, D.
  • Moya Gonzalez, Adolfo
  • Ammari, F.
  • Rutledge, Douglas N.
Tipo de Documento: Artículo
Título de Revista/Publicación: Chemometrics And Intelligent Laboratory Systems
Fecha: Marzo 2012
Volumen: 112
Materias:
Escuela: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Departamento: Ingeniería Rural [hasta 2014]
Grupo Investigación UPM: LPF-TAGRALIA
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Independent Components Analysis is a Blind Source Separation method that aims to find the pure source signals mixed together in unknown proportions in the observed signals under study. It does this by searching for factors which are mutually statistically independent. It can thus be classified among the latent-variable based methods. Like other methods based on latent variables, a careful investigation has to be carried out to find out which factors are significant and which are not. Therefore, it is important to dispose of a validation procedure to decide on the optimal number of independent components to include in the final model. This can be made complicated by the fact that two consecutive models may differ in the order and signs of similarly-indexed ICs. As well, the structure of the extracted sources can change as a function of the number of factors calculated. Two methods for determining the optimal number of ICs are proposed in this article and applied to simulated and real datasets to demonstrate their performance.

Más información

ID de Registro: 12308
Identificador DC: http://oa.upm.es/12308/
Identificador OAI: oai:oa.upm.es:12308
Identificador DOI: 10.1016/j.chemolab.2011.12.005
Depositado por: Memoria Investigacion
Depositado el: 13 Ago 2012 11:19
Ultima Modificación: 21 Abr 2016 11:33
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