How are the centered kernel principal components relevant to regression task? -An exact analysis

Masahiro Yukawa, Klaus Robert Muller, Yuto Ogino

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We present an exact analytic expression of the contributions of the kernel principal components to the relevant information in a nonlinear regression problem. A related study has been presented by Braun, Buhmann, and Müller in 2008, where an upper bound of the contributions was given for a general supervised learning problem but with 'uncentered' kernel PCAs. Our analysis clarifies that the relevant information of a kernel regression under explicit centering operation is contained in a finite number of leading kernel principal components, as in the 'uncentered' kernel-Pca case, if the kernel matches the underlying nonlinear function so that the eigenvalues of the centered kernel matrix decay quickly. We compare the regression performances of the least-square-based methods with the centered and uncentered kernel PCAs by simulations.

    Original languageEnglish
    Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2841-2845
    Number of pages5
    Volume2018-April
    ISBN (Print)9781538646588
    DOIs
    Publication statusPublished - 2018 Sep 10
    Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
    Duration: 2018 Apr 152018 Apr 20

    Other

    Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
    CountryCanada
    CityCalgary
    Period18/4/1518/4/20

    Keywords

    • Kernel PCA
    • Nonlinear regression
    • Reproducing kernel Hilbert space
    • Spectral decomposition

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

    Fingerprint Dive into the research topics of 'How are the centered kernel principal components relevant to regression task? -An exact analysis'. Together they form a unique fingerprint.

  • Cite this

    Yukawa, M., Muller, K. R., & Ogino, Y. (2018). How are the centered kernel principal components relevant to regression task? -An exact analysis. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (Vol. 2018-April, pp. 2841-2845). [8462392] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8462392