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Sparse kernel logistic regression based on L1/2 regularization

Science China(Information Sciences) 2013年第4期56卷 75-90页

作者： XU Chen PENG ZhiMing JING WenFeng Department of Statistics University of British ColumbiaVancouverBC V6T1Z2Canada Institute for Information and System Science School of Mathematics and StatisticsXi'an Jiaotong University

The sparsity driven classification technologies have attracted much attention in recent years,due to their capability of providing more compressive representations and clear *** most popular classification approaches are support vector machines(SVMs) and kernel logistic regression(KLR),each having its own *** sparsification of SVM has been well studied,and many sparse versions of 2-norm SVM,such as 1-norm SVM(1-SVM),have been ***,the sparsifiation of KLR has been less *** existing sparsification of KLR is mainly based on L1 norm and L2 norm penalties,which leads to the sparse versions that yield solutions not so sparse as it should be.A very recent study on L1/2 regularization theory in compressive sensing shows that L1/2 sparse modeling can yield solutions more sparse than those of 1 norm and 2 norm,and,furthermore,the model can be efficiently solved by a simple iterative thresholding *** objective function dealt with in L1/2 regularization theory is,however,of square form,the gradient of which is linear in its variables(such an objective function is the so-called linear gradient function).In this paper,through extending the linear gradient function of L1/2 regularization framework to the logistic function,we propose a novel sparse version of KLR,the 1/2 quasi-norm kernel logistic regression(1/2-KLR).The version integrates advantages of KLR and L1/2 regularization,and defines an efficient implementation scheme of sparse *** suggest a fast iterative thresholding algorithm for 1/2-KLR and prove its *** provide a series of simulations to demonstrate that 1/2-KLR can often obtain more sparse solutions than the existing sparsity driven versions of KLR,at the same or better accuracy *** conclusion is also true even in comparison with sparse SVMs(1-SVM and 2-SVM).We show an exclusive advantage of 1/2-KLR that the regularization parameter in the algorithm can be adaptively set whenever the sparsity(c

关键词： classification L1/2 regularization thresholding algorithm kernel logistic regression support vectors

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Image Manipulation Detection Through Laterally Linked Pixels and Kernel Algorithms

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Computer Systems Science & Engineering 2022年第4期41卷 357-371页

作者： K.K.Thyagharajan G.Nirmala RMD Engineering College KavaraipettaiChennaiIndia

In this paper,copy-move forgery in image is detected for single image with multiple manipulations such as blurring,noise addition,gray scale conver-sion,brightness modiﬁcations,rotation,Hu adjustment,color adjustment,contrast changes and JPEG ***,traditional algorithms detect only copy-move attacks in image and never for different manipulation in single *** proposed LLP(Laterally linked pixel)algorithm has two dimensional arrays and single layer is obtained through unit linking pulsed neural network for detec-tion of copied region and kernel tricks is applied for detection of multiple manip-ulations in single forged *** algorithm consists of two channels such as feeding component(F-Channel)and linking component(L channel)for linking *** algorithm linking pixels detects image with multiple manipulation and copy-move forgery due to one-to-one correspondence between pixel and neu-ron,where each pixel’s intensity is taken as input for F channel of neuron and connected for forgery identiﬁ***,neuron is connected with neighboringﬁeld of neuron by L channel for detecting forged images with multi-ple manipulations in the image along with copy-move,through kernel trick clas-siﬁer(KTC).From experimental results,proposed LLP algorithm performs better than traditional algorithms for multiple manipulated copy and paste *** accuracy obtained through LLP algorithm is about 90%and further forgery detec-tion is improved based on optimized kernel selections in classiﬁcation algorithm.

关键词： Machine learning copy move forgery support vectors kernel feature extraction

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