An in-depth comparasion on FastICA, CuBICA and IC-FastICA

Authors:
Bin Wang;Wenkai Lu
Affiliations:
State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, China;State Key Laboratory of Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

Citing 2
Cited 0

CuBICA: independent component analysis by simultaneous third- and fourth-order cumulant diagonalization

IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis

IEEE Transactions on Neural Networks

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Abstract

FastICA and CuBICA are two remarkable independent component analysis algorithms for dealing with blind signal separation problems. In this paper, we first present a novel ICA estimation algorithm, initialization constrained FastICA (IC-FastICA), through combining the technical merits of these two approaches. Then, a performance comparison study on these three approaches is conducted through the simulations on some standard benchmark data. The experimental results demonstrate that the IC-FastICA achieves higher performances on unmixing error and signal noise ratio while appreciably increasing computation cost.