Name
Title | ||
|---|---|---|
SPATIOTEMPORAL BLIND SOURCE SEPARATION USING DOUBLE-SIDED APPROXIMATE JOINT DIAGONALIZATION |
Abstract | ||
|---|---|---|
In independent component analysis (ICA) the common task is to achieve eitherspatial ortemporal independence by linearly mapping into a feature space. If the data possesses both spatial and temporal structures such as a sequence of images or 3d-scans taken at fixed time intervals, we can require the transformed data to be as indepen- dent as possible in both domains. First introduced by Stone using a joint entropy energy function, spatiotemporal ICA is a promising method for real-world data analysis. We propose a novel algorithm for performing spatiotemporal ICA by jointly diagonalizing various source conditions such as higher-order cumulants of the mixtures, both in time and in space. Similar to algebraic ICA algorithms, this provides a robust method for data analysis, which is confirmed by simulations. |
Year | Venue | Field |
|---|---|---|
2005 | European Signal Processing Conference | Fixed time,Feature vector,Algebraic number,Matrix decomposition,Algorithm,Speech recognition,Cumulant,Joint entropy,Independent component analysis,Blind signal separation,Mathematics |
DocType | ISBN | Citations |
Conference | 978-160-4238-21-1 | 7 |
PageRank | References | Authors |
0.68 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fabian J. Theis | 1 | 931 | 85.37 |
| P. Gruber | 2 | 51 | 5.80 |
| Ingo R. Keck | 3 | 7 | 0.68 |
| Anke Meyer-Bäse | 4 | 15 | 2.58 |
| Elmar Wolfgang Lang | 5 | 260 | 36.10 |