TrIK-SVM : an alternative decomposition for kernel methods in Krein spaces
Machine Learning
2019-02-28 v1
Abstract
The proposed work aims at proposing a alternative kernel decomposition in the context of kernel machines with indefinite kernels. The original paper of KSVM (SVM in Kre\v{i}n spaces) uses the eigen-decomposition, our proposition avoids this decompostion. We explain how it can help in designing an algorithm that won't require to compute the full kernel matrix. Finally we illustrate the good behavior of the proposed method compared to KSVM.
Cite
@article{arxiv.1902.10569,
title = {TrIK-SVM : an alternative decomposition for kernel methods in Krein spaces},
author = {Gaëlle Loosli},
journal= {arXiv preprint arXiv:1902.10569},
year = {2019}
}
Comments
ESANN - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2019, Bruges, Belgium