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Communication Dans Un Congrès Année : 2008

Local Subspace Classifiers: Linear and Nonlinear Approaches

Résumé

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.
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Dates et versions

hal-00203992 , version 1 (21-01-2008)

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Citer

Hakan Cevikalp, Diane Larlus, Matthijs Douze, Frédéric Jurie. Local Subspace Classifiers: Linear and Nonlinear Approaches. MLSP 2007 - IEEE Workshop on Machine Learning for Signal Processing, Aug 2007, Thessaloniki, Greece. pp.57-62, ⟨10.1109/MLSP.2007.4414282⟩. ⟨hal-00203992⟩
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