抄録
Iterative optimization of convex divergence is discussed. The convex divergence is used as a measure of independence for ICA algorithms. An additional method to incorporate supervisory information to reduce the ICA's permutation indeterminacy is also given. Speed of the algorithm is examined using a set of simulated data and brain fMRI data.
| 本文言語 | English |
|---|---|
| ホスト出版物のタイトル | IEEE International Symposium on Information Theory - Proceedings |
| ページ | 214 |
| ページ数 | 1 |
| 出版ステータス | Published - 2003 |
| イベント | Proceedings 2003 IEEE International Symposium on Information Theory (ISIT) - Yokohama, Japan 継続期間: 2003 6月 29 → 2003 7月 4 |
Other
| Other | Proceedings 2003 IEEE International Symposium on Information Theory (ISIT) |
|---|---|
| 国/地域 | Japan |
| City | Yokohama |
| Period | 03/6/29 → 03/7/4 |
ASJC Scopus subject areas
- 電子工学および電気工学