Hypothesis Testing in Independent Component Analysis / Blind Source Separation

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Journal Title

Journal ISSN

Volume Title

Sähkötekniikan korkeakoulu | Master's thesis

Authors

Date

2014-02-10

Department

Major/Subject

Signal Processing

Mcode

S3013

Degree programme

TLT - Master’s Programme in Communications Engineering

Language

en

Pages

52 + 6

Series

Abstract

Independent component analysis (ICA) is a widely used technique for extracting latent (unobserved) source signals from observed multidimensional measurements. Up to date, the estimation problems in ICA are thoroughly studied whereas the statistical inference (hypothesis testing) in ICA is still in its infancy. In this thesis we construct fast and robust bootstrap hypotheses on the coefficients of the mixing matrix in ICA model to investigate contribution of a specific source signal-of-interest onto a specific sensor (mixture variable). Such testing procedure can be used e.g. to detect if a mixture is severely affected by a specific kind of noise or to propose an optimum setup of the recording sensors, which facilitates dimensionality reduction in high-dimensional signal separation problems. The fast and robust bootstrap method has given the tests great potential of being used in real-world ICA analysis of high-dimensional data sets e.g. in biomedical applications.

Description

Supervisor

Koivunen, Visa

Thesis advisor

Ollila, Esa

Keywords

ICA, Hypothesis Testing, Fast and Robust Bootstrap, Confidence Interval

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Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-201403061525

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[dipl] Sähkötekniikan korkeakoulu / ELEC