A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
Neural Computation
Combining Functional Data Projections for Time Series Classification
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Journal of Multivariate Analysis
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Functional data are difficult to manage for many traditional pattern recognition techniques, given the very high (or intrinsically infinite) dimensionality. The reason is that functional data are functions and most algorithms are designed to work with (small) finite-dimensional vectors. In this paper we propose a functional analysis technique to obtain finite-dimensional representations of functional data. The key idea is to consider each functional curve as a point in a general function space and then project these points onto a Reproducing Kernel Hilbert Space with the aid of a Support Vector Machine. We show some theoretical properties of the method and illustrate the performance of the proposed representation in clustering using a real data set.