Name
Title | ||
|---|---|---|
Formulation and integration of learning differential equations on the stiefel manifold. |
Abstract | ||
|---|---|---|
This letter aims at illustrating the relevance of numerical integration of learning differential equations on differential manifolds. In particular, the task of learning with orthonormality constraints is dealt with, which is naturally formulated as an optimization task with the compact Stiefel manifold as neural parameter space. Intrinsic properties of the derived learning algorithms, such as stability and constraints preservation, are illustrated through experiments on minor and independent component analysis (ICA). |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TNN.2005.852860 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
differential manifold,unsupervised neural network learning,orthonormality constraint,constraints preservation,riemannian manifold,riemannian gradient,stiefel manifold,independent component analysis,differential equation,numerical integration,compact stiefel manifold,intrinsic property,optimization task,dierential geometry,geodesics.,neural parameter space,computational geometry,differential geometry,parameter space,difference equations,neural nets,geodesy,unsupervised learning | Differential equation,Riemannian manifold,Computer science,Numerical integration,Stiefel manifold,Unsupervised learning,Artificial intelligence,Differential geometry,Artificial neural network,Machine learning,Manifold | Journal |
Volume | Issue | ISSN |
16 | 6 | 1045-9227 |
Citations | PageRank | References |
9 | 0.76 | 12 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simone Fiori | 1 | 494 | 52.86 |