Multivariate input vector space reconstruction and its application

Author

Xi Jianhui ; Lei, Zhang ; Niu Yanfang ; Ronghui, Su ; Jiang Liying

Author_Institution

Sch. of Autom., Shenyang Aerosp. Univ., Shenyang, China

fYear

2012

fDate

23-25 May 2012

Firstpage

3994

Lastpage

3997

Abstract

This paper was concentrated on the reconstruction of multivariate input vector space. Based on evaluating the nonlinear correlation degree between observed variables and output variables, the input variables were selected if the evaluation was strong. Then, C-C method was used to reconstruct an initial input vector space. Finally, FastICA method was expanded to extract the effective independent information and reduce the dimension of initial input vector. Simulation results showed the effectiveness of the reconstructed input vector.

Keywords

independent component analysis; information retrieval; vectors; C-C method; FastICA method; independent component analysis; independent information extraction; input variables; multivariate input vector space reconstruction; nonlinear correlation degree; observed variables; output variables; Correlation; Input variables; Predictive models; Simulation; Space vehicles; Time series analysis; Vectors; FastICA; Input vector space reconstruction; Nonlinear correlation degree;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2012 24th Chinese

Conference_Location

Taiyuan

Print_ISBN

978-1-4577-2073-4

Type

conf

DOI

10.1109/CCDC.2012.6244636

Filename

6244636