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Nonlinear prediction based on independent component analysis mixture modelling

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Nonlinear prediction based on independent component analysis mixture modelling

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dc.contributor.author Safont Armero, Gonzalo es_ES
dc.contributor.author Salazar Afanador, Addisson es_ES
dc.contributor.author Vergara Domínguez, Luís es_ES
dc.date.accessioned 2015-12-05T11:54:29Z
dc.date.available 2015-12-05T11:54:29Z
dc.date.issued 2011
dc.identifier.issn 0302-9743
dc.identifier.uri http://hdl.handle.net/10251/58589
dc.description.abstract [EN] This paper presents a new algorithm for nonlinear prediction based on independent component analysis mixture modelling (ICAMM). The data are considered from several mutually-exclusive classes which are generated by different ICA models. This strategy allows linear local projections that can be adapted to partial segments of a data set while maintaining generalization (capability for nonlinear modelling) given the mixture of several ICAs. The resulting algorithm is a general purpose technique that could be applied to time series prediction, to recover missing data in images, etc. The performance of the proposed method is demonstrated by simulations in comparison with several classical linear and nonlinear methods. © 2011 Springer-Verlag. es_ES
dc.description.sponsorship This work has been supported by the Generalitat Valenciana under grant PROMETEO/2010/040, and the Spanish Administration and the FEDER Programme of the European Union under grant TEC 2008-02975/TEC.
dc.language Inglés es_ES
dc.publisher Springer Verlag (Germany) es_ES
dc.relation.ispartof Lecture Notes in Computer Science es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject ICA es_ES
dc.subject ICAMM es_ES
dc.subject Kriging es_ES
dc.subject Nonlinear prediction es_ES
dc.subject Wiener structure es_ES
dc.subject Algorithms es_ES
dc.subject Forecasting es_ES
dc.subject Mixtures es_ES
dc.subject Neural networks es_ES
dc.subject Time series es_ES
dc.subject Independent component analysis es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Nonlinear prediction based on independent component analysis mixture modelling es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/978-3-642-21498-1_64
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO%2F2010%2F040/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TEC2008-02975/ES/PROCESADOR NO-LINEAL DE MEZCLAS CON APLICACION EN DETECCION, CLASIFICACION, FILTRADO Y PREDICCION/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L. (2011). Nonlinear prediction based on independent component analysis mixture modelling. Lecture Notes in Computer Science. 6691(1):508-515. https://doi.org/10.1007/978-3-642-21498-1_64 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/978-3-642-21498-1_64 es_ES
dc.description.upvformatpinicio 508 es_ES
dc.description.upvformatpfin 515 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 6691 es_ES
dc.description.issue 1 es_ES
dc.relation.senia 211855 es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.contributor.funder Generalitat Valenciana
dc.description.references Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, New York (2001) es_ES
dc.description.references Lee, T.W., Lewicki, M.S., Sejnowski, T.J.: ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation. IEEE Trans. on Patt. Analysis and Mach. Intellig. 22(10), 1078–1089 (2000) es_ES
dc.description.references Malaroiu, S., Kiviluoto, K., Oja, E.: ICA Preprocessing for Time Series Prediction. In: 2nd International Workshop on ICA and BSS (ICA 2000), pp. 453–457 (2000) es_ES
dc.description.references Pajunen, P.: Extensions of Linear Independent Component Analysis: Neural and Information-Theoretic Methods. Ph.D. Thesis, Helsinki University of Technology (1998) es_ES
dc.description.references Gorriz, J.M., Puntonet, C.G., Salmeron, G., Lang, E.W.: Time Series Prediction using ICA Algorithms. In: Proceedings of the 2nd IEEE International Workshop on Intelligent Data Acquisit. and Advanc. Comput. Systems: Technology and Applications, pp. 226–230 (2003) es_ES
dc.description.references Wang, C.Z., Tan, X.F., Chen, Y.W., Han, X.H., Ito, M., Nishikawa, I.: Independent component analysis-based prediction of O-Linked glycosylation sites in protein using multi-layered neural networks. In: IEEE 10th Internat. Conf. on Signal Processing, pp. 1–4 (2010) es_ES
dc.description.references Zhang, Y., Teng, Y., Zhang, Y.: Complex process quality prediction using modified kernel partial least squares. Chemical Engineering Science 65, 2153–2158 (2010) es_ES
dc.description.references Salazar, A., Vergara, L., Serrano, A., Igual, J.: A general procedure for learning mixtures of independent component analyzers. Pattern Recognition 43(1), 69–85 (2010) es_ES
dc.description.references Bersektas, D.: Nonlinear programming. Athena Scientific, Massachusetts (1999) es_ES
dc.description.references Cardoso, J.F., Souloumiac, A.: Blind beamforming for non gaussian signals. IEE Proceedings-F 140(6), 362–370 (1993) es_ES
dc.description.references Salazar, A., Vergara, L., Llinares, R.: Learning material defect patterns by separating mixtures of independent component analyzers from NDT sonic signals. Mechanical Systems and Signal processing 24(6), 1870–1886 (2010) es_ES
dc.description.references Salazar, A., Vergara, L.: ICA mixtures applied to ultrasonic nondestructive classification of archaeological ceramics. EURASIP Journal on Advances in Signal Processing, vol. 2010, p.11, Article ID 12520111 (2010), doi:10.1155/2010/125201 es_ES
dc.description.references Salazar, A., Vergara, L., Miralles, R.: On including sequential dependence in ICA mixture models. Signal Processing 90(7), 2314–2318 (2010) es_ES
dc.description.references Raghavan, R.S.: A Model for Spatially Correlated Radar Clutter. IEEE Trans. on Aerospace and Electronic Systems 27, 268–275 (1991) es_ES


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