Feature space approximation for kernel-based supervised learning

• URL: http://arxiv.org/abs/2011.12651v2
• Date: Mon, 15 Mar 2021 17:06:34 GMT
• Title: Feature space approximation for kernel-based supervised learning
• Authors: Patrick Gel{\ss}, Stefan Klus, Ingmar Schuster, Christof Sch\"utte
• Abstract summary: The goal is to reduce the size of the training data, resulting in lower storage consumption and computational complexity. 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.
• Score: 2.653409741248232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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