Paper Info
Title
Fast and robust bootstrap method for testing hypotheses in the ICA model
Abstract
Independent component analysis (ICA) is a widely used technique for extracting latent (unobserved) source signals from observed multidimensional measurements. In this paper we construct a fast and robust bootstrap (FRB) method for testing hypotheses on elements of the mixing matrix in the ICA model. The FRB method can be devised for estimators which are solutions to fixed-point (FP) equations. In this paper we develop FRB test for the widely popular FastICA estimator. The developed test can be used in real-world ICA analysis of high-dimensional data sets seen e.g. in big data analysis, as it avoids the common obstacles of conventional bootstrap such as immense computational cost and lack of robustness. Moreover, instability and convergence problems of the Fast ICA algorithm when applied to bootstrap data are prevented. Simulations and examples illustrate the usefulness and validity of the developed test.
Year
DOI
Venue
2014
10.1109/ICASSP.2014.6853547
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
independent component analysis,FRB method,FastICA estimator,ICA model,big data analysis,fast and robust bootstrap method,fixed-point equations,independent component analysis,latent source signal extraction,FastICA,bootstrap,hypothesis testing,independent component analysis
Convergence (routing),Data set,Pattern recognition,Computer science,Robustness (computer science),Artificial intelligence,FastICA,Independent component analysis,Statistical hypothesis testing,Bootstrapping (electronics),Estimator
Conference
ISSN
Citations 
PageRank 
1520-6149
3
0.73
References 
Authors
3
3
Name
Order
Citations
PageRank
Shahab Basiri1123.01
Esa Ollila235133.51
Visa Koivunen31917187.81