Abstract
In this paper we introduce a new embedding technique to linearly project labeled data samples into a new space where the performance of a Nearest Neighbor classifier is improved. The approach is based on considering a large set of simple discriminant projections and finding the subset with higher classification performance. In order to implement the feature selection process we propose the use of the adaboost algorithm. The performance of this technique is tested in a multiclass classification problem related to the production of cork stoppers for wine bottles.
Keywords
- Linear Discriminant Analysis
- Scatter Matrix
- Adaboost Algorithm
- Feature Extraction Process
- Feature Selection Process
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download to read the full chapter text
Chapter PDF
References
Fisher, R.: On subharmonic solutions of a Hamiltonian system. The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)
Aladjem, M.: Linear discriminant analysis for two classes via removal of classification structure. IEEE Trans. Pattern Anal. Machine Intell. 19(2), 187–192 (1997)
Fukunaga, K., Mantock, J.: Nonparametric discriminant analysis. IEEE Trans. Pattern Anal. Machine Intell. 5(6), 671–678 (1983)
Devijver, P., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice Hall, London (1982)
Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, Boston (1990)
Bressan, M., Vitria, J.: Nonparametric discriminant analysis and nearest neighbor classification. Pattern Recognition Letters 24(15), 2743–2749 (2003)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conference on CVPR, Kauai, Hawaii, pp. 511–518 (2001)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)
Schapire, R.E.: A brief introduction to boosting. In: IJCAI, pp. 1401–1406 (1999)
Radeva, P., Bressan, M., Tobar, A., Vitrià, J.: Bayesian Classification for Inspection of Industrial Products. In: Escrig Monferrer, M.T., Toledo, F., Golobardes, E. (eds.) Topics in Artificial Intelligence. Sringer Verlag Series: Lecture Notes in Computer Science, vol. 2504, pp. 399–407 (2002)
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
Radeva, P., Vitrià, J. (2004). Discriminant Projections Embedding for Nearest Neighbor Classification. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2004. Lecture Notes in Computer Science, vol 3287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30463-0_38
Download citation
DOI: https://doi.org/10.1007/978-3-540-30463-0_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23527-9
Online ISBN: 978-3-540-30463-0
eBook Packages: Springer Book Archive