Abstract
In this paper, I introduce a concept of differential decorrelation which finds a linear mapping that minimizes the concurrent change of variables. Motivated by the differential anti-Hebbian rule [1], I develop a natural gradient algorithm for differential decorrelation and present its local stability analysis. The algorithm is successfully applied to the task of nonstationary source separation © Springer-Verlag Berlin Heidelberg 2002.
Cite
CITATION STYLE
Choi, S. (2002). Adaptive differential decorrelation: A natural gradient algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 1168–1173). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_189
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
