A Theory for Learning by Weight Flow on Stiefel-Grassman Manifold
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Publication:2746421
DOI10.1162/089976601750265036zbMath0982.68111OpenAlexW2169736182MaRDI QIDQ2746421
Publication date: 10 October 2001
Published in: Neural Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1162/089976601750265036
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Generalized neural networks for spectral analysis: dynamics and Liapunov functions ⋮ Preserving geometric properties of the exponential matrix by block Krylov subspace methods ⋮ Blind Separation of Positive Sources by Globally Convergent Gradient Search ⋮ Lie-group-type neural system learning by manifold retractions ⋮ Nonlinear Complex-Valued Extensions of Hebbian Learning: An Essay ⋮ Neural learning by geometric integration of reduced `rigid-body' equations ⋮ A fast and adaptive ICA algorithm with its application to fetal electrocardiogram extraction ⋮ Descent methods for optimization on homogeneous manifolds
Cites Work
- Global optimization and stochastic differential equations
- Independent component analysis by general nonlinear Hebbian-like learning rules
- Neural networks for blind separation with unknown number of sources
- Independent component analysis, a new concept?
- Dynamical systems that sort lists, diagonalize matrices, and solve linear programming problems
- Richtungsfelder und Fernparallelismus in \(n\)-dimensionalen Mannigfaltigkeiten
- The Geometry of Algorithms with Orthogonality Constraints
- HIGH-ORDER CONTRASTS FOR SELF-ADAPTIVE SOURCE SEPARATION
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