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Novel linear search for support vector machine parameter selection

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Journal of Zhejiang University-Science C(Computers and Electronics) 2011年第11期12卷 885-896页

作者： Hong-xia PANG Wen-de DONG Zhi-hai XU Hua-jun FENG Qi LI Yue-ting CHEN State Key Laboratory of Optical Instrumentation Zhejiang University Hangzhou 310027 China

Selecting the optimal parameters for support vector machine (SVM) has long been a hot research topic. Aiming for support vector classification/regression (SVC/SVR) with the radial basis function (RBF) kernel, we summarize the rough line rule of the penalty parameter and kernel width, and propose a novel linear search method to obtain these two optimal parameters. We use a direct-setting method with thresholds to set the epsilon parameter of SVR. The proposed method directly locates the right search field, which greatly saves computing time and achieves a stable, high accuracy. The method is more competitive for both SVC and SVR. It is easy to use and feasible for a new data set without any adjustments, since it requires no parameters to set.

关键词： Support vector machine (SVM) Rough line rule Parameter selection linear search Motion prediction

Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第8期15卷 664-674页

作者： Nu WEN Shi-zhi YANG Cheng-jie ZHU Sheng-cheng CUI Anhui Institute of Optics and Fine Mechanics Chinese Academy of Sciences Key Laboratory of Optical Calibration and Characterization Chinese Academy of Sciences University of Chinese Academy of Sciences

In this paper, we present an adaptive two-step contourlet-wavelet iterative shrinkage/thresholding （TcwlST） algorithm for remote sensing image restoration. This algorithm can be used to deal with various linear inverse problems （LIPs）, including image deconvolution and reconstruction. This algorithm is a new version of the famous two-step iterative shrinkage/thresholding （TWIST） algorithm. First, we use the split Bregrnan Rudin-Osher-Fatemi （ROF） model, based on a sparse dictionary, to decompose the image into cartoon and texture parts, which are represented by wavelet and contourlet, respectively. Second, we use an adaptive method to estimate the regularization parameter and the shrinkage threshold. Finally, we use a linear search method to find a step length and a fast method to accelerate convergence. Results show that our method can achieve a signal-to-noise ratio improvement （ISNR） for image restoration and high convergence speed.

关键词： Image restoration Adaptive Cartoon-texture decomposition linear search lterative shrinkage/thresholding

A New Method of Moving Asymptotes for Large-scale linearly Equality-constrained Minimization

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Acta Mathematicae Applicatae Sinica 2011年第2期27卷 317-328页

作者： Hai-jun Wang Qin Ni Hao Liu College of Sciences China University of Mining and Technology Xuzhou 221008 China College of Sciences Nanjing University of Aeronautics and Astronautics Nanjing 210016 China College of Sciences Nanjing University of Technology Nanjing 210019 China

A new method of moving asymptotes for large-scale minimization subject to linear equality constraints is discussed. In this method, linear equality constraints are deleted with null space technique and the descending direction is obtained by solving a convex separable subproblem of moving asymptotes in each iteration. New rules for controlling the asymptotes parameters are designed and the global convergence of the method under some reasonable conditions is established and proved. The numerical results show that the new method may be capable of processing some large scale problems.

关键词： method of moving asymptotes trust region linear search large scale linear equality constraints