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Kernel-based functional principal components

โœ Scribed by Graciela Boente; Ricardo Fraiman

Book ID
104301391
Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
122 KB
Volume
48
Category
Article
ISSN
0167-7152

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โœฆ Synopsis

In this paper, we propose kernel-based smooth estimates of the functional principal components when data are continuous trajectories of stochastic processes. Strong consistency and the asymptotic distribution are derived under mild conditions.

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The dominant set of eigenvectors of the symmetrical kernel Gram matrix is used in many important kernel methods (like e.g. kernel Principal Component Analysis, feature approximation, denoising, compression, prediction) in the machine learning area. Yet in the case of dynamic and/or large-scale data,