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