Theis, Fabian J. and Gruber, Peter and Keck, I. R. and Meyer-Bäse, A. and Lang, Elmar (2005) Spatiotemporal blind source separation using double-sided approximate joint diagonalization. In: 13. European Signal Processing Conference, EUSIPCO 2005; 4 - 8 September 2005, Antalya, Turkey; proceedings; conference CD. UNSPECIFIED.
Full text not available from this repository.
In independent component analysis (ICA) the common task is to achieve either spatial or temporal 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 independent 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.
|Item Type:||Book Section|
|Institutions:||Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang|
|Subjects:||500 Science > 570 Life sciences|
|Created at the University of Regensburg:||Unknown|
|Deposited On:||15 Oct 2010 08:51|
|Last Modified:||15 Oct 2010 08:51|