Longitudinal Support Vector Machines for High Dimensional Time Series

We consider the problem of learning a classifier from observed functional data. Here, each data-point takes the form of a single time-series and contains numerous features. Assuming that each such series comes with a binary label, the problem of learning to predict the label of a new coming time-ser...

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Bibliographic Details
Published inarXiv.org
Main Authors Pelckmans, Kristiaan, Hong-Li, Zeng
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 22.02.2020
Subjects
Algorithms
Computational geometry
Convexity
Empirical analysis
Learning
Multivariate analysis
Optimization
Support vector machines
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Summary:We consider the problem of learning a classifier from observed functional data. Here, each data-point takes the form of a single time-series and contains numerous features. Assuming that each such series comes with a binary label, the problem of learning to predict the label of a new coming time-series is considered. Hereto, the notion of {\em margin} underlying the classical support vector machine is extended to the continuous version for such data. The longitudinal support vector machine is also a convex optimization problem and its dual form is derived as well. Empirical results for specified cases with significance tests indicate the efficacy of this innovative algorithm for analyzing such long-term multivariate data.
ISSN:2331-8422