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  • Selecting the corner in the L–curve approach to Tikhonov regularisation.

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    35292_1.pdf (126.6Kb)
    Author(s)
    Johnston, PR
    Gulrajani, RM
    Griffith University Author(s)
    Johnston, Peter R.
    Year published
    2000
    Metadata
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    Abstract
    The performance of two methods for selecting the corner in the L-curve approach to Tikhonov regularization is evaluated via computer simulation. These methods are selecting the corner as the point of maximum curvature in the L-curve, and selecting it as the point where the product of abcissa and ordinate is a minimum. It is shown that both these methods resulted in significantly better regularization parameters than that obtained with an often-used empirical Composite REsidual and Smoothing Operator approach, particularly in conditions where correlated geometry noise exceeds Gaussian measurement noise. It is also shown that ...
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    The performance of two methods for selecting the corner in the L-curve approach to Tikhonov regularization is evaluated via computer simulation. These methods are selecting the corner as the point of maximum curvature in the L-curve, and selecting it as the point where the product of abcissa and ordinate is a minimum. It is shown that both these methods resulted in significantly better regularization parameters than that obtained with an often-used empirical Composite REsidual and Smoothing Operator approach, particularly in conditions where correlated geometry noise exceeds Gaussian measurement noise. It is also shown that the regularization parameter that results with the minimum-product method is identical to that selected with another empirical zero-crossing approach proposed earlier.
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    Journal Title
    IEEE Trans. Biomedical Eng
    Volume
    47
    Issue
    9
    DOI
    https://doi.org/10.1109/10.867966
    Copyright Statement
    © 2000 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Artificial Intelligence and Image Processing
    Biomedical Engineering
    Electrical and Electronic Engineering
    Publication URI
    http://hdl.handle.net/10072/55850
    Collection
    • Journal articles

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