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

Jouan-Rimbaud Bouveresse, D. and Moya Gonzalez, Adolfo and Ammari, F. and 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.

Description

Title: Two Novel Methods For The Determination Of The Number Of Components In Independent Components Analysis Models
Author/s:
  • Jouan-Rimbaud Bouveresse, D.
  • Moya Gonzalez, Adolfo
  • Ammari, F.
  • Rutledge, Douglas N.
Item Type: Article
Título de Revista/Publicación: Chemometrics And Intelligent Laboratory Systems
Date: March 2012
ISSN: 0169-7439
Volume: 112
Subjects:
Faculty: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Department: Ingeniería Rural [hasta 2014]
UPM's Research Group: LPF-TAGRALIA
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview

Abstract

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.

More information

Item ID: 12308
DC Identifier: http://oa.upm.es/12308/
OAI Identifier: oai:oa.upm.es:12308
DOI: 10.1016/j.chemolab.2011.12.005
Deposited by: Memoria Investigacion
Deposited on: 13 Aug 2012 11:19
Last Modified: 21 Apr 2016 11:33
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM