Tighter mean-squared error bounds on kurtosis-based Fast-ICA

Matthew D. Kleffner, Douglas L. Jones

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

FastICA is a widely-used independent component analysis technique for blindly separating mixtures of instantaneously-mixed, independent sources recorded with multiple sensors. When using FastICA to estimate one source in interference, the unbiased mean-squared error can be bounded from above by the Schniter-Tong bounds on Shalvi-Weinstein estimators. We derive tighter upper bounds by extending both the Schniter-Tong proof and the Schniter-Johnson proof of upper bounds on constant-modulus estimators. These tighter bounds also exist over a wider range of sources and channels; existence gaps of over an order of magnitude of minimum-mean-squared error have been observed.

Original languageEnglish (US)
Title of host publication2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings
Pages556-560
Number of pages5
DOIs
StatePublished - Dec 1 2007
Event2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007 - Madison, WI, United States
Duration: Aug 26 2007Aug 29 2007

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings

Other

Other2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007
CountryUnited States
CityMadison, WI
Period8/26/078/29/07

ASJC Scopus subject areas

  • Signal Processing

Fingerprint Dive into the research topics of 'Tighter mean-squared error bounds on kurtosis-based Fast-ICA'. Together they form a unique fingerprint.

Cite this