首页> 外文会议>International Conference on Scale-Space and PDE Methods in Computer Vision; 20050407-09; Hofgeismar(DE) >Relations Between Higher Order TV Regularization and Support Vector Regression
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Relations Between Higher Order TV Regularization and Support Vector Regression

机译:高阶电视正则化与支持向量回归之间的关系

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We study the connection between higher order total variation (TV) regularization and support vector regression (SVR) with spline kernels in a one-dimensional discrete setting. We prove that the contact problem arising in the tube formulation of the TV minimization problem is equivalent to the SVR problem. Since the SVR problem can be solved by standard quadratic programming methods this provides us with an algorithm for the solution of the contact problem even for higher order derivatives. Our numerical experiments illustrate the approach for various orders of derivatives and show its close relation to corresponding nonlinear diffusion and diffusion-reaction equations.
机译:我们研究在一维离散设置中用样条内核的高阶总变异(TV)正则化与支持向量回归(SVR)之间的联系。我们证明了在电视最小化问题的管公式中出现的接触问题等同于SVR问题。由于SVR问题可以通过标准的二次编程方法解决,这为我们提供了一种解决接触问题的算法,即使对于高阶导数也是如此。我们的数值实验说明了各种阶导数的方法,并显示了它与相应的非线性扩散和扩散反应方程的密切关系。

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