Flexible independent component analysis

Seungjin Choi; Andrzej Cichocki; Shunichi Amari

Flexible independent component analysis

Seungjin Choi
, Andrzej Cichocki
, Shunichi Amari

Chungbuk National University

研究成果: 会議への寄与 › 論文 › 査読

52 被引用数 (Scopus)

抄録

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.

本文言語	英語
ページ	83-92
ページ数	10
出版ステータス	出版済み - 1998
外部発表	はい
イベント	Proceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII - Cambridge, Engl 継続期間: 31 8月 1998 → 2 9月 1998

会議

会議	Proceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII
City	Cambridge, Engl
Period	31/08/98 → 2/09/98

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引用スタイル

@conference{97f8ecbbed0747eb853359fdab2d5167,

title = "Flexible independent component analysis",

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.",

author = "Seungjin Choi and Andrzej Cichocki and Shunichi Amari",

year = "1998",

language = "英語",

pages = "83--92",

note = "Proceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII ; Conference date: 31-08-1998 Through 02-09-1998",

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TY - CONF

T1 - Flexible independent component analysis

AU - Choi, Seungjin

AU - Cichocki, Andrzej

AU - Amari, Shunichi

PY - 1998

Y1 - 1998

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/0031634640

M3 - 論文

AN - SCOPUS:0031634640

SP - 83

EP - 92

T2 - Proceedings of the 1998 8th IEEE Workshop on Neural Networks for Signal Processing VIII

Y2 - 31 August 1998 through 2 September 1998

ER -

Flexible independent component analysis

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