On Geodesic Exponential Kernels

Aasa  Feragen; François Lauze; Søren Hauberg

doi:10.1007/978-3-319-24261-3

On Geodesic Exponential Kernels

Aasa Feragen
, François Lauze
, Søren Hauberg

University of Copenhagen

Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Research › peer-review

Abstract

This extended abstract summarizes work presented at CVPR 2015 [1].

Standard statistics and machine learning tools require input data residing in a Euclidean space. However, many types of data are more faithfully represented in general nonlinear metric spaces or Riemannian manifolds, e.g. shapes, symmetric positive definite matrices, human poses or graphs. The underlying metric space captures domain specific knowledge, e.g. non-linear constraints, which is available a priori. The intrinsic geodesic metric encodes this knowledge, often leading to improved statistical models.

Original language	English
Title of host publication	Proceedings of the 3rd International Workshop on Similarity-Based Pattern Recognition (SIMBAD 2015)
Editors	Aasa Feragen, Marco Loog, Marcello Pelillo
Publisher	Springer
Publication date	2015
Pages	211-213
ISBN (Print)	978-3-319-24260-6
ISBN (Electronic)	978-3-319-24261-3
DOIs	https://doi.org/10.1007/978-3-319-24261-3
Publication status	Published - 2015
Event	3rd International Workshop on Similarity-Based Pattern Recognition (SIMBAD 2015) - Copenhagen, Denmark Duration: 12 Oct 2015 → 14 Oct 2015 Conference number: 3 http://www.dsi.unive.it/~simbad/2015/

Workshop

Workshop	3rd International Workshop on Similarity-Based Pattern Recognition (SIMBAD 2015)
Number	3
Country/Territory	Denmark
City	Copenhagen
Period	12/10/2015 → 14/10/2015
Internet address	http://www.dsi.unive.it/~simbad/2015/

Series	Lecture Notes in Computer Science
Volume	9370
ISSN	0302-9743

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10.1007/978-3-319-24261-3

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title = "On Geodesic Exponential Kernels",

abstract = "This extended abstract summarizes work presented at CVPR 2015 [1]. Standard statistics and machine learning tools require input data residing in a Euclidean space. However, many types of data are more faithfully represented in general nonlinear metric spaces or Riemannian manifolds, e.g. shapes, symmetric positive definite matrices, human poses or graphs. The underlying metric space captures domain specific knowledge, e.g. non-linear constraints, which is available a priori. The intrinsic geodesic metric encodes this knowledge, often leading to improved statistical models.",

author = "Aasa Feragen and Fran{\c c}ois Lauze and S{\o}ren Hauberg",

year = "2015",

doi = "10.1007/978-3-319-24261-3",

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booktitle = "Proceedings of the 3rd International Workshop on Similarity-Based Pattern Recognition (SIMBAD 2015)",

note = "3rd International Workshop on Similarity-Based Pattern Recognition (SIMBAD 2015), SIMBAD ; Conference date: 12-10-2015 Through 14-10-2015",

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Feragen, A, Lauze, F & Hauberg, S 2015, On Geodesic Exponential Kernels. in A Feragen, M Loog & M Pelillo (eds), Proceedings of the 3rd International Workshop on Similarity-Based Pattern Recognition (SIMBAD 2015). Springer, Lecture Notes in Computer Science, vol. 9370, pp. 211-213, 3rd International Workshop on Similarity-Based Pattern Recognition (SIMBAD 2015), Copenhagen, Denmark, 12/10/2015. https://doi.org/10.1007/978-3-319-24261-3

On Geodesic Exponential Kernels. / Feragen, Aasa ; Lauze, François; Hauberg, Søren.
Proceedings of the 3rd International Workshop on Similarity-Based Pattern Recognition (SIMBAD 2015). ed. / Aasa Feragen; Marco Loog; Marcello Pelillo. Springer, 2015. p. 211-213 (Lecture Notes in Computer Science, Vol. 9370).

Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Research › peer-review

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AU - Lauze, François

AU - Hauberg, Søren

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N2 - This extended abstract summarizes work presented at CVPR 2015 [1]. Standard statistics and machine learning tools require input data residing in a Euclidean space. However, many types of data are more faithfully represented in general nonlinear metric spaces or Riemannian manifolds, e.g. shapes, symmetric positive definite matrices, human poses or graphs. The underlying metric space captures domain specific knowledge, e.g. non-linear constraints, which is available a priori. The intrinsic geodesic metric encodes this knowledge, often leading to improved statistical models.

AB - This extended abstract summarizes work presented at CVPR 2015 [1]. Standard statistics and machine learning tools require input data residing in a Euclidean space. However, many types of data are more faithfully represented in general nonlinear metric spaces or Riemannian manifolds, e.g. shapes, symmetric positive definite matrices, human poses or graphs. The underlying metric space captures domain specific knowledge, e.g. non-linear constraints, which is available a priori. The intrinsic geodesic metric encodes this knowledge, often leading to improved statistical models.

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M3 - Conference abstract in proceedings

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BT - Proceedings of the 3rd International Workshop on Similarity-Based Pattern Recognition (SIMBAD 2015)

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PB - Springer

T2 - 3rd International Workshop on Similarity-Based Pattern Recognition (SIMBAD 2015)

Y2 - 12 October 2015 through 14 October 2015

ER -

On Geodesic Exponential Kernels

Abstract

Workshop

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