Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm

By Rafael Blanquero, Emilio Carrizosa, Asunción Jiménez-Cordero, Belén Martín-Barragán. 2019

20 citations recorded by the source corpus.

Abstract Functional Data Analysis (FDA) is devoted to the study of data which are functions. Support Vector Machine (SVM) is a benchmark tool for classification, in particular, of functional data. SVM is frequently used with a kernel (e.g.: Gaussian) which involves a scalar bandwidth parameter. In this paper, we propose to use kernels with functional bandwidths. In this way, accuracy may be improved, and the time intervals critical for classification are identified. Tuning the functional parameters of the new kernel is a challenging task expressed as a continuous optimization problem, solved by means of a heuristic. Our experiments with benchmark data sets show the advantages of using functional parameters and the effectiveness of our approach.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Selection of time instants and intervals with Support Vector Regression for multivariate functional data (2020, methodology): “Such tools are computationally very expensive, and thus we propose, instead, an alternating approach, as also done in Blanquero et al., 2019a, 2019b; Jiménez-Cordero and Maldonado, 2020.”