PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Automatic Adjustment of Discriminant Adaptive Nearest Neighbor
Nicolas Delannay, Cedric Archambeau and Michel Verleysen
In: 18th International Conference on Pattern Recognition, 20-24 August 2006, Hong Kong, PRC.

Abstract

K-Nearest Neighbors relies on the definition of a global metric. In contrast, Discriminant Adaptive Nearest Neighbor (DANN) computes a different metric at each query point based on a local Linear Discriminant Analysis. In this paper, we propose a technique to automatically adjust the hyper-parameters in DANN by the optimization of two quality criteria. The first one measures the quality of discrimination, while the second one maximizes the local class homogeneity. We use a Bayesian formulation to prevent overfitting.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2376
Deposited By:Cedric Archambeau
Deposited On:22 November 2006