Independent component analysis by entropy maximization (infomax)
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Authors
Garvey, Jennie Hill
Subjects
Advisors
Kragh, Frank E.
Date of Issue
2007-06
Date
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
This thesis explores the "Infomax" method of Independent Component Analysis (ICA) to accomplish blind source separation (BSS). The Infomax method separates unknown source signals from a number of signal mixtures by maximizing the entropy of a transformed set of signal mixtures and is accomplished by performing gradient ascent in MATLAB. This work specifically focuses on small numbers of two types of signals: audio signals and simple communications signals (polar non-return to zero signals). The Infomax method is found to be successful and efficient only for small numbers of signals, and improvements to the gradient ascent algorithm should be made for the Infomax algorithm to succeed for more than three signal mixtures. MATLAB implementation code is included as appendices.
Type
Thesis
Description
Series/Report No
Department
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
xviii, 105 p. : ill. (some col.) ;
Citation
Distribution Statement
Approved for public release; distribution is unlimited.