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Efficient Leave-One-Out Evaluation of Kernelized Implicit Mappings

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Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13813))

Abstract

End-to-end learning is discussed in the framework of linear combinations of reproducing kernels associated with training samples. This paper shows that the leave-one-out (LOO) technique can be executed very efficiently in this framework. It is a simple extension of previous fast LOO algorithms for scalar-valued functions to vector-valued functions, but opens the door for multiple analyses of the same data with almost no cost. With a newly defined LOO matrix, we demonstrate the effectiveness and the universality of this approach.

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Acknowledgment

This work was partially supported by JSPS KAKENHI (Grant Number 19H04128).

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Authors and Affiliations

  1. Division of Computer Science and Information Technology, Graduate School of Information Science and Technology, Hokkaido University, Sapporo, 060-0814, Japan

    Mineichi Kudo, Keigo Kimura & Shumpei Morishita

  2. School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China

    Lu Sun

Authors
  1. Mineichi Kudo
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  2. Keigo Kimura
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  3. Shumpei Morishita
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  4. Lu Sun
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Corresponding author

Correspondence to Mineichi Kudo .

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Editors and Affiliations

  1. Concordia University, Montreal, QC, Canada

    Adam Krzyzak

  2. Concordia University, Montreal, QC, Canada

    Ching Y. Suen

  3. Ca Foscari University of Venice, Venezia, Italy

    Andrea Torsello

  4. Concordia University, Montreal, QC, Canada

    Nicola Nobile

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Kudo, M., Kimura, K., Morishita, S., Sun, L. (2022). Efficient Leave-One-Out Evaluation of Kernelized Implicit Mappings. In: Krzyzak, A., Suen, C.Y., Torsello, A., Nobile, N. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2022. Lecture Notes in Computer Science, vol 13813. Springer, Cham. https://doi.org/10.1007/978-3-031-23028-8_23

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  • DOI: https://doi.org/10.1007/978-3-031-23028-8_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23027-1

  • Online ISBN: 978-3-031-23028-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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