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Numerical differentiation of noisy data with local optimum by data segmentation

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Journal of Systems Engineering and Electronics 2015年第4期26卷 868-876页

作者： Jianhua Zhang Xiufu Que Wei Chen Yuanhao Huang Lianqiao Yang Key Laboratory of Advanced Display and System Applications (Shanghai University) Ministry of Education School of Mechanical & Electronic Engineering and Automation Shanghai University

A new numerical differentiation method with local opti- mum by data segmentation is proposed. The segmentation of data is based on the second derivatives computed by a Fourier devel- opment method. A filtering process is used to achieve acceptable segmentation. Numerical results are presented by using the data segmentation method, compared with the regularization method. For further investigation, the proposed algorithm is applied to the resistance capacitance （RC） networks identification problem, and improvements of the result are obtained by using this algorithm.

关键词： numerical differentiation noisy data local optimum,data segmentation.

Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

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Journal of the Operations Research Society of China 2022年第1期10卷 89-112页

作者： Jia-Bin Zhou Yan-Qin Bai Yan-Ru Guo Hai-Xiang Lin Department of Mathematics Shanghai UniversityShanghai 200444China Delft Institute of Applied Mathematics Delft University of TechnologyDelft 2600GAThe Netherlands

In general,data contain noises which come from faulty instruments,flawed measurements or faulty communication.Learning with data in the context of classification or regression is inevitably affected by noises in the data.In order to remove or greatly reduce the impact of noises,we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine(Lap-TSVM).A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine(IFLap-TSVM)is presented.Moreover,we extend the linear IFLap-TSVM to the nonlinear case by kernel function.The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classi-fier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization.Experiments with constructed artificial datasets,several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine(TSVM),intuitionistic fuzzy twin support vector machine(IFTSVM)and Lap-TSVM.

关键词： Twin support vector machine Semi-supervised classification Intuitionistic fuzzy Manifold regularization noisy data

Toward Effective Knowledge Acquisition with First—Order Logic Induction

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Journal of Computer Science & Technology 2002年第5期17卷 565-577页

作者：张晓龙 Masyuki Numao IT Solution Delivery AXA Life Insurance Company Tokyo 194-0022 Japan Department of Computer Science Tokyo Institute of Technology Tokyo 152-8552 Japan

Knowledge acquisition with machine learning techniques is a fundamental re-quirement for knowledge discovery from databases and data mining systems. Two techniquesin particular - inductive learning and theory revision - have been used toward this end. Amethod that combines both approaches to effectively acquire theories (regularity) from a setof training examples is presented. Inductive learning is used to acquire new regularity fromthe training examples; and theory revision is used to improve an initial theory. In addition, atheory preference criterion that is a combination of the MDL-based heuristic and the Laplaceestimate has been successfully employed in the selection of the promising theory. The resultingalgorithm developed by integrating inductive learning and theory revision and using the criterionhas the ability to deal with complex problems, obtaining useful theories in terms of its predictiveaccuracy.

关键词： knowledge acquisition inductive logic programming theory revision the MDL principle the Laplace estimate noisy data

Collaborative Matrix Factorization with Soft Regularization for Drug-Target Interaction Prediction

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Journal of Computer Science & Technology 2021年第2期36卷 310-322页

作者： Li-Gang Gao Meng-Yun Yang Jian-Xin Wang School of Computer Science and Engineering Central South UniversityChangsha 410083China Hunan Provincial Key Laboratory of Bioinformatics Central South UniversityChangsha 410083China School of Science Shaoyang UniversityShaoyang 422000China

Identifying the potential drug-target interactions(DTI)is critical in drug discovery.The drug-target interaction prediction methods based on collaborative filtering have demonstrated attractive prediction performance.However,many corresponding models cannot accurately express the relationship between similarity features and DTI features.In order to rationally represent the correlation,we propose a novel matrix factorization method,so-called collaborative matrix factorization with soft regularization(SRCMF).SRCMF improves the prediction performance by combining the drug and the target similarity information with matrix factorization.In contrast to general collaborative matrix factorization,the fundamental idea of SRCMF is to make the similarity features and the potential features of DTI approximate,not identical.Specifically,SRCMF obtains low-rank feature representations of drug similarity and target similarity,and then uses a soft regularization term to constrain the approximation between drug(target)similarity features and drug(target)potential features of DTI.To comprehensively evaluate the prediction performance of SRCMF,we conduct cross-validation experiments under three different settings.In terms of the area under the precision-recall curve(AUPR),SRCMF achieves better prediction results than six state-of-the-art methods.Besides,under different noise levels of similarity data,the prediction performance of SRCMF is much better than that of collaborative matrix factorization.In conclusion,SRCMF is robust leading to performance improvement in drug-target interaction prediction.

关键词： drug-target interaction collaborative matrix factorization soft regularization noisy data

Point Sources Identification Problems for Heat Equations

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Communications in Computational Physics 2009年第5期5卷 897-913页

作者： Leevan Ling Tomoya Takeuchi Department of Mathematics Hong Kong Baptist UniversityKowloon TongHong Kong Graduate School of Mathematical Sciences University of Tokyo3-8-1 KomabaMeguroTokyo153-8914Japan

We considered the point source identification problems for heat equations from noisy observation data taken at the minimum number of spatially fixed measurement points.We aim to identify the unknown number of sources and their locations along with their strengths.In our previous work,we proved that minimum measurement points needed under the noise-free setting.In this paper,we extend the proof to cover the noisy cases over a border class of source functions.We show that if the regularization parameter is chosen properly,the problem can be transformed into a poles identification problem.A reconstruction scheme is proposed on the basis of the developed theoretical results.Numerical demonstrations in 2D and 3D conclude the paper.

关键词： Point source source identification heat equations noisy data convergence

Robust sequential design for piecewise-stationary multi-armed bandit problem in the presence of outliers

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Statistical Theory and Related Fields 2021年第2期5卷 122-133页

作者： Yaping Wang Zhicheng Peng Riquan Zhang Qian Xiao KLATASDS-MOE School of StatisticsEast China Normal UniversityShanghaiPeople’s Republic of China Ant Group HangzhouPeople’s Republic of China Department of Statistics University of GeorgiaAthensGAUSA

The multi-armed bandit(MAB)problem studies the sequential decision making in the presence of uncertainty and partial feedback on rewards.Its name comes from imagining a gambler at a row of slot machines who needs to decide the best strategy on the number of times as well as the orders to play each machine.It is a classic reinforcement learning problem which is fundamental to many online learning problems.In many practical applications of the MAB,the reward distributions may change at unknown time steps and the outliers(extreme rewards)often exist.Current sequential design strategies may struggle in such cases,as they tend to infer additional change points to fit the outliers.In this paper,we propose a robust change-detection upper confidence bound(RCD-UCB)algorithm which can distinguish the real change points from the outliers in piecewise-stationary MAB settings.We show that the proposed RCD-UCB algorithm can achieve a nearly optimal regret bound on the order of O(√SKT log T),where T is the number of time steps,K is the number of arms and S is the number of stationary segments.We demonstrate its superior performance compared to some state-of-the-art algorithms in both simulation experiments and real data analysis.(See https://***/woaishufenke/MAB_STRF.git for the codes used in this paper.)

关键词： Change-point detection noisy data online learning truncated loss

Displacement fields denoising and strains extraction by finite element method

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Theoretical & Applied Mechanics Letters 2011年第1期1卷 18-21页

作者： B.Q.Guo,~(1,a)) H.M.Xie,~(1,b)) Y.J.Li,~1P.W.Chen,~2and Q.M.Zhang~2 1) AML,Department of Engineering Mechanics,Tsinghua University,Beijing 100084,China 2) State Key Laboratory of Explosion Science and Technology,Beijing Institute of Technology,Beijing 100081, China Tsinghua Univ AML Dept Engn Mech Beijing 100084 Peoples R China Beijing Inst Technol State Key Lab Explos Sci & Technol Beijing 100081 Peoples R China

Optical full-field measurement methods are now widely applied in various domains. In general,the displacement fields can be directly obtained from the measurement,however in mechanical analysis strain fields are preferred.To extract strain fields from noisy displacement fields is always a challenging topic.In this study,a finite element method for smoothing displacement fields and calculating strain fields is proposed.An experimental test case on a holed aluminum specimen under tension is applied to validate this method.The heterogeneous displacement fields are measured by digital image correlation(DIC).By this proposed method,the result shows that the measuring noise on experimental displacement fields can be successfully removed,and strain fields can be reconstructed in the arbitrary area.

关键词： noisy data smoothing strain differentiation finite element method

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噪声环境下的不协调信息系统知识约简

噪声环境下的不协调信息系统知识约简

第二十二届中国数据库学术会议

作者：高建龙李梅梅佘剑纯胡子敬范裕北京大学信息科学技术学院信息科学中心

1引言在不协调信息系统的条件下,粗糙集理论给出了一些独特的发现可能规则的方法。但是各种经典约简方法,都有一个局限性就是它所处理的分类必须是完全正确的,或是肯定的,因

关键词： noisy data Attribute reduction A decision information system

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基于可变精度粗集的规则简化方法

基于可变精度粗集的规则简化方法