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    Approximate Kernel Selection via Matrix Approximation

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    Thumbnail
    Name:
    TNNLS-191120_Xin.pdf
    Size:
    435.8Kb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Article
    Authors
    Ding, Lizhong cc
    Liao, Shizhong cc
    Liu, Yong cc
    Liu, Li
    Zhu, Fan
    Yao, Yazhou cc
    Shao, Ling cc
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Structural and Functional Bioinformatics Group
    Date
    2020-01-14
    Online Publication Date
    2020-01-14
    Print Publication Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/661036
    
    Metadata
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    Abstract
    Kernel selection is of fundamental importance for the generalization of kernel methods. This article proposes an approximate approach for kernel selection by exploiting the approximability of kernel selection and the computational virtue of kernel matrix approximation. We define approximate consistency to measure the approximability of the kernel selection problem. Based on the analysis of approximate consistency, we solve the theoretical problem of whether, under what conditions, and at what speed, the approximate criterion is close to the accurate, one, establishing the foundations of approximate kernel selection. We introduce two selection criteria based on error estimation and prove the approximate consistency of the multilevel circulant matrix (MCM) approximation and Nyström approximation under these criteria. Under the theoretical guarantees of the approximate consistency, we design approximate algorithms for kernel selection, which exploits the computational advantages of the MCM and Nyström approximations to conduct kernel selection in a linear or quasi-linear complexity. We experimentally validate the theoretical results for the approximate consistency and evaluate the effectiveness of the proposed kernel selection algorithms.
    Citation
    Ding, L., Liao, S., Liu, Y., Liu, L., Zhu, F., Yao, Y., … Gao, X. (2020). Approximate Kernel Selection via Matrix Approximation. IEEE Transactions on Neural Networks and Learning Systems, 1–11. doi:10.1109/tnnls.2019.2958922
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Neural Networks and Learning Systems
    DOI
    10.1109/tnnls.2019.2958922
    Additional Links
    https://ieeexplore.ieee.org/document/8959405/
    ae974a485f413a2113503eed53cd6c53
    10.1109/tnnls.2019.2958922
    Scopus Count
    Collections
    Articles; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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