Prototype Selection for Finding Efficient Representations of Dissimilarity Data
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Support vector machine for functional data classification
Neurocomputing
Support vector regression methods for functional data
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Classification of three-way data by the dissimilarity representation
Signal Processing
A study on the influence of shape in classifying small spectral data sets
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
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The classification of unknown samples is among the most common problems found in chemometrics. For this purpose, a proper representation of the data is very important. Nowadays, chemical spectral data are analyzed as vectors of discretized data where the variables have not connection, and other aspects of their functional nature e.g. shape differences (structural), are also ignored. In this paper, we study some advanced representations for chemical spectral datasets, and for that we make a comparison of the classification results of 4 datasets by using their traditional representation and two other: Functional Data Analysis and Dissimilarity Representation. These approaches allow taking into account the information that is missing in the traditional representation, thus better classification results can be achieved. Some suggestions are made about the more suitable dissimilarity measures to use for chemical spectral data.