Memory Efficient Kernel Approximation for Non-Stationary and Indefinite Kernels
Published in 2022 International Joint Conference on Neural Networks (IJCNN), 2022
This paper originated from my bachelor thesis and is all about large scale kernel methods. We explored the memory efficient approximation of kernel matrices that lead to invalid kernels. We proposed to correct the eigenspectrum with a shift computed via the Lanczos-Iteration. This correction leads to stable downstream tasks and despite having a large approximation error, competitive scores on the tasks. Download paper here
Recommended citation: S. Heilig, M. Münch and F. -M. Schleif, "Memory Efficient Kernel Approximation for Non-Stationary and Indefinite Kernels," 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022, pp. 1-8, doi: 10.1109/IJCNN55064.2022.9892153.