Abstract
A tunable nearest neighbor (TNN) classifier is proposed to handle the discrimination problems. The TNN borrows the concept of feature line spaces from the nearest feature line (NFL) classifier, to make use of the information implied by the interaction between each pair of points in the same class. Instead of the NFL distance, a tunable distance metric is proposed in the TNN. The experimental evaluation shows that in the given feature space, the TNN consistently achieves better performance than NFL and conventional nearest neighbor methods, especially for the tasks with small training sets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Li, S.Z., Lu, J.: Face recognition using the nearest feature line method. IEEE Trans. Neural Networks 10(2), 439–443 (1999)
Li, S.Z., Chan, K.L., Wang, C.L.: Performance evaluation of the nearest feature line method in image classification and retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 22(11) (November 2000)
Zhao, L., Qi, W., Li, S.Z., Yang, S.Q., Zhang, H.J.: A new content-based shot retrieval approach: key-frame extraction based earest feature line (NFL) classification. In: ACM Multimedia Information Retrieval 2000, Los Angeles, USA (October 2000)
Cover, T.M., Hart, P.: Nearest neighbor pattern classification. Annals of Statistics (1967)
Hastie, T., Tibshirani, R.: Discriminant adaptive nearest neighbor classfication. IEEE Trans. Pattern Analysis and Machine Intelligence 18(6) (June 1996)
Martinez, A.M., Benavente, R.: The AR-face database. CVC Technical Report 24 (June 1998)
Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. On Pattern Analysis and Machine Intelligence 23(2) (February 2001)
Goldberger, J., Gordon, S., Greenspan, H.: An efficient image similarity measure based on approximations of KL-Divergence between two gaussian mixtures. In: Proceedings of the 9th IEEE International Conference on Computer Vision
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, Y., Zhang, C., Wang, J. (2004). Tunable Nearest Neighbor Classifier. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-28649-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22945-2
Online ISBN: 978-3-540-28649-3
eBook Packages: Springer Book Archive