The Representation of Chemical Spectral Data for Classification
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Similarity-based classification of sequences using hidden Markov models
Pattern Recognition
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The nearest neighbor (N N) ru le is a simple and intuitive method for solving classification problems. Originally, it uses distances to the complete training set. It performs well, however, it is sensitive to noisy objects, due to its operation on local neighborhoods only . A more global approach is possible by mapping the distance data on to a pseudo-Euclidean space, such that the distances are preserved as well as possible. Then, a classifier built in such a space can out perform the NN rule. However, again all objects from th e training set are used for a projection of new data.This paper addresses the issue of reducing the training set while possibly preserving the original structure of the mapped data. Some criteria are introduced and evaluated against two problems, polygon recognition and digit recognition. Our experiments show that the representation mismatch criterion is beneficial for the applications considered. Moreover, the linear classifier built in the pseudo-Euclidean space, determined by 20% -25% of the training objects, outperforms the NN rule based on all of them.