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    Towards Large Scale Spectral Problems via Diffusion Process

    Access Status
    Fulltext not available
    Authors
    Li, Q.
    Liu, Wan-Quan
    Li, Ling
    Wang, R.
    Date
    2017
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Li, Q. and Liu, W. and Li, L. and Wang, R. 2017. Towards Large Scale Spectral Problems via Diffusion Process. International Conference on Digital Image Computing - Techniques and Applications (DICTA), pp. 112-118: IEEE.
    Source Title
    DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications
    Source Conference
    IEEE Catalog Number: ISBN: CFP17397-POD 978-1-5386-2840-9 2017 International Conference on Digital Image Computing: Techniques and Applications
    DOI
    10.1109/DICTA.2017.8227498
    ISBN
    9781538628393
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/69318
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 IEEE. Spectral methods refer to the problem of finding eigenvectors of an affinity matrix. Despite promising performance on revealing manifold structure, they are limited in its applicability to large-scale problems due to the high computational cost of eigendecomposition. Nyström method, as a classic method, seeks an approximate solution by first solving a smaller eigenproblem defined on a subset of landmarks, and then extrapolating the eigenvectors of all points through the linear combinations of landmarks. In this paper, we embed a simple yet effective diffusion process into Nyström formula so that we can utilize all data points rather than only landmarks to set up the reduced eigenproblem and estimate the out-of-sample embedding. We apply our method on both dimension reduction and spectral clustering problems. Extensive experiments show that the proposed method can reduce the approximation error with fewer landmarks and less run time.

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