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Distribution-free learning theory for approximating submanifolds from reptile motion capture data.
- Source :
-
Computational Mechanics . Aug2021, Vol. 68 Issue 2, p337-356. 20p. - Publication Year :
- 2021
-
Abstract
- This paper describes the formulation and experimental testing of an estimation of submanifold models of animal motion. It is assumed that the animal motion is supported on a configuration manifold, Q, and that the manifold is homeomorphic to a known smooth, Riemannian manifold, S. Estimation of the configuration submanifold is achieved by finding an unknown mapping, γ , from S to Q. The overall problem is cast as a distribution-free learning problem over the manifold of measurements. This paper defines sufficient conditions that show that the rates of convergence in L μ 2 (S) of approximations of γ correspond to those known for classical distribution-free learning theory over Euclidean space. This paper concludes with a study and discussion of the performance of the proposed method using samples from recent reptile motion studies. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01787675
- Volume :
- 68
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Computational Mechanics
- Publication Type :
- Academic Journal
- Accession number :
- 151332826
- Full Text :
- https://doi.org/10.1007/s00466-021-02034-0