Flexible independent component analysis

Seungjin Choi, Andrzej Cichocki, Shunichi Amari

Research output: Contribution to conferencePaperpeer-review

51 Citations (Scopus)

Abstract

We present a flexible independent component analysis (ICA) algorithm which can separate mixtures of sub- and super-Gaussian source signals with self-adaptive nonlinearities. The flexible ICA algorithm in the framework of natural Riemannian gradient, is derived using the parameterized generalized Gaussian density model. The nonlinear function in the flexible ICA algorithm is self-adaptive and is controlled by Gaussian exponent. Computer simulation results confirm the validity and high performance of the proposed algorithm.

Original languageEnglish
Pages83-92
Number of pages10
Publication statusPublished - 1998
Externally publishedYes
EventProceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII - Cambridge, Engl
Duration: 31 Aug 19982 Sep 1998

Conference

ConferenceProceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII
CityCambridge, Engl
Period31/08/982/09/98

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