Automatic adjustment of discriminant adaptive nearest neighbor
(2006) 18th International Conference on Pattern Recognition (ICPR 2006) — Location: Hong Kong (China) (20.August.2006)
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- 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 over-fitting.
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Delannay, N., Archambeau, C., & Verleysen, M. (2006). Automatic adjustment of discriminant adaptive nearest neighbor. In Tang, Y.Y.; Wang, S.P.; Lorette, G.L.; Yeung, D.S.; Yan, H.; (ed.), Proceedings of the 18th International Conference on Pattern Recognition (ICPR 2006) (p. 4 pages). IEEE comput. soc. https://hdl.handle.net/2078.5/253979