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preprint · arXiv (Cornell University) · 2014

Learning with Algebraic Invariances, and the Invariant Kernel Trick

Franz J. Király, Andreas Ziehe, Klaus‐Robert Müller

Cited by 1Open access

Abstract

When solving data analysis problems it is important to integrate prior knowledge and/or structural invariances. This paper contributes by a novel framework for incorporating algebraic invariance structure into kernels. In particular, we show that algebraic properties such as sign symmetries in data, phase independence, scaling etc. can be included easily by essentially performing the kernel trick twice. We demonstrate the usefulness of our theory in simulations on selected applications such as sign-invariant spectral clustering and underdetermined ICA.

Cite this paper

APA 7

Király, F. J., Ziehe, A., & Müller, K. (2014). Learning with Algebraic Invariances, and the Invariant Kernel Trick. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1411.7817

MLA 9

Király, Franz J., et al. “Learning with Algebraic Invariances, and the Invariant Kernel Trick.” arXiv (Cornell University), 2014, https://doi.org/10.48550/arxiv.1411.7817.

Chicago

Király, Franz J., Andreas Ziehe, and Klaus‐Robert Müller. 2014. “Learning with Algebraic Invariances, and the Invariant Kernel Trick.” arXiv (Cornell University). https://doi.org/10.48550/arxiv.1411.7817.

Harvard

Király, F.J., Ziehe, A. and Müller, K. (2014) ‘Learning with Algebraic Invariances, and the Invariant Kernel Trick’, arXiv (Cornell University). Available at: https://doi.org/10.48550/arxiv.1411.7817.

BibTeX
@misc{kiraly2014learning,
  title = {{Learning with Algebraic Invariances, and the Invariant Kernel Trick}},
  author = {Király, Franz J. and Ziehe, Andreas and Müller, Klaus‐Robert},
  journal = {arXiv (Cornell University)},
  year = {2014},
  doi = {10.48550/arxiv.1411.7817}
}

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