HAL - INRIA :: [hal-00203992, version 1] Local Subspace Classifiers: Linear and Nonlinear Approaches

hal-00203992, version 1

Local Subspace Classifiers: Linear and Nonlinear Approaches

Hakan Cevikalp ^1, 2, Diane Larlus ^1, 2, Matthijs Douze ^1, 2, Frédéric Jurie () ^1, 2

IEEE Workshop on Machine Learning for Signal Processing (2007) 1551-2541

Abstract: The K-local hyperplane distance nearest neighbor (HKNN) algorithm is a local classification method which builds nonlinear decision surfaces directly in the original sample space by using local linear manifolds. Although the HKNN method has been successfully applied in several classification tasks, it is not possible to employ distance metrics other than the Euclidean distances in this scheme, which can be considered as a major limitation of the method.

1: LEAR (INRIA Grenoble Rhône-Alpes / LJK Laboratoire Jean Kuntzmann)
CNRS : FR71 – CNRS : UMR5527 – INRIA – Laboratoire Jean Kuntzmann – Université Joseph Fourier - Grenoble I – Institut National Polytechnique de Grenoble (INPG)
2: Laboratoire Jean Kuntzmann (LJK)
CNRS : UMR5224 – Université Joseph Fourier - Grenoble I – Université Pierre Mendès-France - Grenoble II – Institut Polytechnique de Grenoble - Grenoble Institute of Technology

hal-00203992, version 1
http://hal.archives-ouvertes.fr/hal-00203992
oai:hal.archives-ouvertes.fr:hal-00203992
From: Véronique Rocher <>
Submitted on: Monday, 21 January 2008 14:23:36
Updated on: Friday, 3 December 2010 10:02:04

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DOI: 10.1109/MLSP.2007.4414282

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