An approach of moment-based algorithm for noisy ICA models

Daisuke Ito; Noboru Murata

An approach of moment-based algorithm for noisy ICA models

先進理工学部

研究成果: Article › 査読

抄録

Factor analysis is well known technique to uncorrelate observed signals with Gaussina noises before ICA (Independent Component Analysis) algorithms are applied. However, factor analysis is not applicable when the number of source signals are more than that of Ledermann's bound, and when the observations are contaminated by non-Gaussian noises. In this paper, an approach is proposed based on higher-order moments of signals and noises in order to overcome those constraints.

本文言語	English
ページ（範囲）	343-349
ページ数	7
ジャーナル	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
巻	3195
出版ステータス	Published - 2004 12 1

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

文献へのアクセス

フィンガープリント「An approach of moment-based algorithm for noisy ICA models」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル

@article{94a73d68141e43c985d720ea8d78b67d,

title = "An approach of moment-based algorithm for noisy ICA models",

abstract = "Factor analysis is well known technique to uncorrelate observed signals with Gaussina noises before ICA (Independent Component Analysis) algorithms are applied. However, factor analysis is not applicable when the number of source signals are more than that of Ledermann's bound, and when the observations are contaminated by non-Gaussian noises. In this paper, an approach is proposed based on higher-order moments of signals and noises in order to overcome those constraints.",

author = "Daisuke Ito and Noboru Murata",

year = "2004",

month = dec,

day = "1",

language = "English",

volume = "3195",

pages = "343--349",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Springer Verlag",

}

TY - JOUR

T1 - An approach of moment-based algorithm for noisy ICA models

AU - Ito, Daisuke

AU - Murata, Noboru

PY - 2004/12/1

Y1 - 2004/12/1

N2 - Factor analysis is well known technique to uncorrelate observed signals with Gaussina noises before ICA (Independent Component Analysis) algorithms are applied. However, factor analysis is not applicable when the number of source signals are more than that of Ledermann's bound, and when the observations are contaminated by non-Gaussian noises. In this paper, an approach is proposed based on higher-order moments of signals and noises in order to overcome those constraints.

AB - Factor analysis is well known technique to uncorrelate observed signals with Gaussina noises before ICA (Independent Component Analysis) algorithms are applied. However, factor analysis is not applicable when the number of source signals are more than that of Ledermann's bound, and when the observations are contaminated by non-Gaussian noises. In this paper, an approach is proposed based on higher-order moments of signals and noises in order to overcome those constraints.

UR - http://www.scopus.com/inward/record.url?scp=35048814990&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=35048814990&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:35048814990

VL - 3195

SP - 343

EP - 349

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -