Feature space approximation for kernel-based supervised learning

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.

References in corpus (10)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Machine learning and the physical sciences
• Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification
• Improving CUR Matrix Decomposition and the Nyström Approximation via Adaptive Sampling
• Eigendecompositions of Transfer Operators in Reproducing Kernel Hilbert Spaces
• Multidimensional approximation of nonlinear dynamical systems
• Constraining chemical networks inAstrochemistry
• Tensor-based algorithms for image classification
• Kernel Methods for Surrogate Modeling
• Solving Interpretable Kernel Dimension Reduction