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.
| 元の言語 | 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 |
| 出版物ステータス | Published - 2004 |
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ASJC Scopus subject areas
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science
これを引用
An approach of moment-based algorithm for noisy ICA models. / Ito, Daisuke; Murata, Noboru.
：: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 巻 3195, 2004, p. 343-349.研究成果: Article
}
TY - JOUR
T1 - An approach of moment-based algorithm for noisy ICA models
AU - Ito, Daisuke
AU - Murata, Noboru
PY - 2004
Y1 - 2004
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.
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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 -