Non-parametric Regression between Riemannian Manifolds

Steinke, F; Hein, M

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Non-parametric Regression between Riemannian Manifolds

Steinke, F., & Hein, M. (2009). Non-parametric Regression between Riemannian Manifolds. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems 21 (pp. 1561-1568). Red Hook, NY, USA: Curran.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C497-4 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-DE5B-F

Genre: Conference Paper

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https://papers.nips.cc/paper/3561-non-parametric-regression-between-manifolds.pdf (Publisher version) Open Access status unknown

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Creators:
Steinke, F^{1, 2}, Author
Hein, M, Author

Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794

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Abstract: This paper discusses non-parametric regression between Riemannian manifolds. This learning problem arises frequently in many application areas ranging from signal processing, computer vision, over robotics to computer graphics. We present a new algorithmic scheme for the solution of this general learning problem based on regularized empirical risk minimization. The regularization functional takes into account the geometry of input and output manifold, and we show that it implements a prior which is particularly natural. Moreover, we demonstrate that our algorithm performs well in a difficult surface registration problem.

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Dates: Date issued: 2009-06

Publication Status: Issued

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Identifiers: BibTex Citekey: 5469

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Title: Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)

Place of Event: Vancouver, BC, Canada

Start-/End Date: 2008-12-08 - 2008-12-10

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Title: Advances in neural information processing systems 21

Source Genre: Proceedings

Creator(s):
Koller, D, Editor
Schuurmans, D, Editor
Bengio, Y, Editor
Bottou, L, Editor

Affiliations:
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Publ. Info: Red Hook, NY, USA : Curran

Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1561 - 1568 Identifier: ISBN: 978-1-60560-949-2