Feature space approximation for kernel-based supervised learning

doi:10.1016/j.knosys.2021.106935

Feature space approximation for kernel-based supervised learning

2021-06-01

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DOI：10.1016/j.knosys.2021.106935

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We propose a method for the approximation of high-or even infinite-dimensional feature vectors, which play an important role in supervised learning. The goal is to reduce the size of the training data, resulting in lower storage consumption and computational complexity. Furthermore, the method can be regarded as a regularization technique, which improves the generalizability of learned target functions. We demonstrate significant improvements in comparison to the computation of data driven predictions involving the full training data set. The method is applied to classification and regression problems from different application areas such as image recognition, system identification, and oceanographic time series analysis. (c) 2021 Elsevier B.V. All rights reserved.

Keywords:

Supervised learning

Kernel-based methods

Feature spaces

Dimensionality reduction

System identification

Feature space approximation for kernel-based supervised learning

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