检索结果-南通市图书馆

kernel method-based fuzzy clustering algorithm

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2005年第1期16卷 160-166页

作者： WuZhongdong GaoXinbo XieWeixin YuJianping SchoolofElectronicEngineering XidianUniversityXi'an710071P.R.China CollegeofInformationEngineering ShenzhenUniversityShenzhen518060P.R.China SchoolofElectronicEngineering XidianUniversityXi'an710071P.R.China

The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.

关键词： fuzzy clustering analysis kernel method fuzzy C-means clustering.

Privacy-Preserving Recommendation Based on kernel method in Cloud Computing

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第1期66卷 779-791页

作者： Tao Li Qi Qian Yongjun Ren Yongzhen Ren Jinyue Xia College of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China College of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China College of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China International Business Machines Corporation(IBM) NYUSA

The application field of the Internet of Things(IoT)involves all aspects,and its application in the fields of industry,agriculture,environment,transportation,logistics,security and other infrastructure has effectively promoted the intelligent development of these aspects.Although the IoT has gradually grown in recent years,there are still many problems that need to be overcome in terms of technology,management,cost,policy,and security.We need to constantly weigh the benefits of trusting IoT products and the risk of leaking private data.To avoid the leakage and loss of various user data,this paper developed a hybrid algorithm of kernel function and random perturbation method based on the algorithm of non-negative matrix factorization,which realizes personalized recommendation and solves the problem of user privacy data protection in the process of personalized recommendation.Compared to non-negative matrix factorization privacy-preserving algorithm,the new algorithm does not need to know the detailed information of the data,only need to know the connection between each data;and the new algorithm can process the data points with negative characteristics.Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of preserving users’personal privacy.

关键词： IoT kernel method privacy-preserving personalized recommendation random perturbation

Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Theoretical & Applied Mechanics Letters 2021年第5期11卷 260-265页

作者： Yoshihiro Kanno Mathematics and Informatics Center The University of TokyoHongo 7-3-1Tokyo 113-8656Japan

Data-driven computing in elasticity attempts to directly use experimental data on material,without constructing an empirical model of the constitutive relation,to predict an equilibrium state of a structure subjected to a specified external load.Provided that a data set comprising stress-strain pairs of material is available,a data-driven method using the kernel method and the regularized least-squares was developed to extract a manifold on which the points in the data set approximately lie(Kanno 2021,Jpn.J.Ind.Appl.Math.).From the perspective of physical experiments,stress field cannot be directly measured,while displacement and force fields are measurable.In this study,we extend the previous kernel method to the situation that pairs of displacement and force,instead of pairs of stress and strain,are available as an input data set.A new regularized least-squares problem is formulated in this problem setting,and an alternating minimization algorithm is proposed to solve the problem.

关键词： Alternating minimization Regularized least-squares kernel method Manifold learning Data-driven computing

Fault Pattern Recognition based on kernel method and Fuzzy C-means

在线全文

学校读者我要写书评

暂无评论

International Journal of Plant Engineering and Management 2016年第4期21卷 231-240页

作者： SUN Yebei ZHAO Rongzhen TANG Xiaobin School of Mechanical & Electronic Engineering Lanzhou University of Technology

A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.

关键词： kernel method fuzzy C-means FCM pattern recognition clustering

A New Classifier for Imbalanced Data Based on a Generalized Density Ratio Model

在线全文

学校读者我要写书评

暂无评论

Communications in Mathematics and Statistics 2023年第2期11卷 369-401页

作者： Junjun Li Wenquan Cui Department of Statistics and Finance School of ManagementUniversity of Science and Technology of ChinaHefei 230026People’s Republic of China

Achieving higher true positive rate when decreasing false positive rate is always a great challenge to the imbalance learning community.This work combines penalized empirical likelihood method,lower bound algorithm and Nyströmmethod and applies these techniques along with kernel method to density ratio model.The resulting classifier,density ratio classifier(DRC),is a combination of kernelization,regularization,efficient implementation and threshold moving,all of which are critical to enable DRC to be an effective and powerful method for solving difficult imbalance problems.Compared with other methods,DRC is competitive in that it is widely applicable and it is simple and easy to use without additional imbalance handling skills.In addition,the convergence rate of the estimate of log density ratio is discussed as well.And the results of numerical analysis also show that DRC outperforms other methods in AUC and G-mean score.

关键词： Classifier Density ratio model Imbalance problems kernel method ROC curve

Stressed portfolio optimization with semiparametric method

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Financial Innovation 2022年第1期8卷 821-854页

作者： Chuan-Hsiang Han Kun Wang Department of Quantitative Finance National Tsing Hua UniversityHsinchu 30013Taiwan International Intercollegiate Ph.D.Program National Tsing Hua UniversityHsinchu 30013Taiwan

Tail risk is a classic topic in stressed portfolio optimization to treat unprecedented risks,while the traditional mean–variance approach may fail to perform well.This study proposes an innovative semiparametric method consisting of two modeling components:the nonparametric estimation and copula method for each marginal distribution of the portfolio and their joint distribution,respectively.We then focus on the optimal weights of the stressed portfolio and its optimal scale beyond the Gaussian restriction.Empirical studies include statistical estimation for the semiparametric method,risk measure minimization for optimal weights,and value measure maximization for the optimal scale to enlarge the investment.From the outputs of short-term and long-term data analysis,optimal stressed portfolios demonstrate the advantages of model flexibility to account for tail risk over the traditional mean–variance method.

关键词： Portfolio optimization Tail risk Semiparametric method kernel method Copula method Risk measure Risk-sensitive value measure Scaling effect

kernel-based Nonlinear Fit with Total Least Square（TLS） method

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Kernel-based Nonlinear Fit with Total Least Square（TLS） Me...

kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process

第二十六届中国控制会议

作者： Hu Guanghua,Fu Guanghui School of Mathematics and Statistics,Yunnan University,Kunming 650091,P.R.China

In this paper,on the basis of linear fit in the total least square（TLS）method sense,we proposed a method of nonlinear fit in the TLS method sense via kernel representation.Namely,by using an appropriate kernel function,the problems of nonlinear fit can be transformed to the problems of linear fit without paying the computational penalty and without the precondition that the fitting function type of the data points is known.The experimental results show that the algorithm presented in this paper is available.

关键词： Linear fit Nonlinear fit kernel method Total Least Square（TLS）method

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1465-1476页

作者： Donglei Zheng Le Zhou Zhihuan Song State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou 310027China Automation and Electrical Engineering Zhejiang University of Science&TechnologyHangzhou 310023China

In practical process industries,a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes,which indicates that the measurements coming from different sources are collected at different sampling rates.To build a complete process monitoring strategy,all these multi-rate measurements should be considered for data-based modeling and monitoring.In this paper,a novel kernel multi-rate probabilistic principal component analysis(K-MPPCA)model is proposed to extract the nonlinear correlations among different sampling rates.In the proposed model,the model parameters are calibrated using the kernel trick and the expectation-maximum(EM)algorithm.Also,the corresponding fault detection methods based on the nonlinear features are developed.Finally,a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.

关键词： Fault detection kernel method multi-rate process probability principal component analysis(PPCA)

kernel matrix learning with a general regularized risk functional criterion

在线全文

学校读者我要写书评

暂无评论

Journal of Systems Engineering and Electronics 2010年第1期21卷 72-80页

作者： Chengqun Wang Jiming Chen Chonghai Hu Youxian Sun State Key Laboratory of Industrial Control Technology Department Control Science and Engineering Zhejiang University Hangzhou 310027 P. R. China Department of Mathematics Zhejiang University Hangzhou 310027 E R. China

kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.

关键词： kernel method support vector machine kernel matrix learning HKRS geometric distribution regularized risk functional criterion.