Longitudinal Support Vector Machines for High Dimensional Time Series
Machine Learning
2020-02-25 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.2002.09763,
title = {Longitudinal Support Vector Machines for High Dimensional Time Series},
author = {Kristiaan Pelckmans and Hong-Li Zeng},
journal= {arXiv preprint arXiv:2002.09763},
year = {2020}
}