Ziegaus, Ch. and Lang, Elmar (2004) A neural implementation of the JADE algorithm using higher-order neurons. Neurocomputing 56, pp. 79-100.
Full text not available from this repository.
A neural implementation of the JADE algorithm, called nJADE, is developed which adaptively determines the mixing matrices to be jointly diagonalized with the JADE algorithm. This alleviates the problem of algebraically determining these mixing matrices which becomes a very tedious if not impossible undertaking with high dimensional data. The new learning rule uses higher-order neurons and generalizes Oja's PCA learning rule. As a test case the new nJADE algorithm is applied to high dimensional natural image ensembles to learn appropriate edge filter structures. Quantitative comparison concerning various filter characteristics is made with results obtained with a probabilistic ICA algorithm with kernel-based source density estimation.
|Institutions:||Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang|
|Projects:||Graduiertenkolleg Nichtlinearität und Nichtgleichgewicht|
|Subjects:||500 Science > 530 Physics|
500 Science > 570 Life sciences
|Refereed:||Yes, this version has been refereed|
|Created at the University of Regensburg:||Yes|
|Deposited On:||20 Mar 2007|
|Last Modified:||04 Oct 2010 08:22|