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Sparse Reduced-Rank Regression with outlier detection

Chinese Quarterly Journal of Mathematics 2021年第3期36卷 275-287页

作者： LIANG Bing-jie School of Data Science University of Science and Technology of ChinaHefei 230026China.

Based on the multivariate mean-shift regression model,we propose a new sparse reduced-rank regression approach to achieve low-rank sparse estimation and outlier detection simultaneously.A sparse mean-shift matrix is introduced in the model to indicate outliers.The rank constraint and the group-lasso type penalty for the coefficient matrix encourage the low-rank row sparse structure of coefficient matrix and help to achieve dimension reduction and variable selection.An algorithm is developed for solving our problem.In our simulation and real-data application,our new method shows competitive performance compared to other methods.

关键词： Reduced-rank regression Sparsity outlier detection Group-lasso type penalty

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outlier detection Method Based on SVM and Its Application in Copper-matte Converting

Outlier Detection Method Based on SVM and Its Application in...

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2010 Chinese Control and Decision Conference

作者： Xiaoqi Peng~(1,2),Jun Chen~(1,3),Hongyuan Shen~3 1.School of Information Science and Engineering,Central South University,Changsha,410083,China 2.Department of Information Science and Engineering,Hunan First Normal University,Changsha,410002,China 3.Institute of Information and Electrical Engineering,Hunan University of Science and Technology,Xiangtan,411201,China

＜正＞outlier detection can be treated as a part of the data preprocess or as the object of data mining.There is still no effective detection method for the high-dimensional nonlinear outlier samples.This paper presents an outlier detection method based on support vector machine（SVM）.A SVM model built by the clean sample set without outlier is used to predict the samples,when the error between the prediction-value and actual value exceeds the threshold,the sample is taken as an outlier,otherwise a normal one.The present outlier detection method has been applied to analyze the practical copper-matte converting production data.The results show that this method can efficiently and correctly detect the high dimensional nonlinear outlier sample and has considerable practical value.

关键词： Data Mining outlier detection Support Vector Machine Copper-matte Converting

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Improvement of Industrial System Identification by outlier detection

Improvement of Industrial System Identification by Outlier D...

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第30届中国控制与决策会议

作者： Jing Jiang Liaoning Water Conservancy Vocational College

This paper focuses on improving data environment in the context of industrial process control.Considering the negative influence of outliers in target set,this paper proposes a novel outlier detection method named triSVDD,where three varying SVDD models are built successively.With data processed by triSVDD,performance of data-based techniques dedicating to system identification can be improved significantly.A realistic application,i.e.Transonic Wind Tunnel system is used to verify the significance of triSVDD in terms of system identification.Finally,performance with respect to only outlier detection is also investigated and compared with two well-known competitors.

关键词： outlier detection system identification one-class classification SVDD

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Real-time Angle outlier detection Based on Robust Preprocessing and Feature Extraction

Real-time Angle Outlier Detection Based on Robust Preprocess...

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第37届中国控制会议

作者： Yiwei Ma Chong Dai Gang He Changhao Piao Chengyong Zhu Bo Liu Zhenhui Yao Huiqian Yang Yongxue Bai Smart Energy Technology Research Technology College of AutomationChongqing University of Posts and Telecommunications Chongqing Changan New Energy Automobile Co. Ltd

For the traditional algorithm does not apply well in high-dimensional data stream and detecting potential outliers timely in the case of traditional outlier detection,A robust preprocessing and feature extraction method for real time angle based outlier detection algorithm is proposed to improve stability of irregular data sets and high dimensional spaces of the traditional algorithm.First,k-nearest neighbor and similarity method are applied for data clustering analysis.Then using angle vector to extract the key feature to lessen the data dimension,improving the computation speed of outlier detection algorithm in high dimensional space.Finally,the method based on outlier detection of angular distribution is used to identify outliers in high-dimensional data streams.The experimental results in real-world data sets corroborated that the suggested approach,which can improve the robustness of the algorithm in high-dimensional space,will optimize the storage space and reduce the time cost of the algorithm without loss of accuracy case,and provides a theoretical basis for the rapid detection of high dimensional outliers in the battery system model.

关键词： high-dimensional data streams preprocessing angle based outlier detection feature extraction

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Toward better QSAR/QSPR modeling:simultaneous outlier detection and variable selection using distribution of model features

Toward better QSAR/QSPR modeling:simultaneous outlier detect...

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第十一届全国计算（机）化学学术会议

作者：曹东升梁逸曾许青松李洪东 Research center of modernization of traditional Chinese medicines Central South UniversityChangsha 410083P.R.China School of Mathematical Sciences and Computing Technology Central South UniversityChangsha 41 0083P.R.China

＜正＞Building a robust and reliable QSAR/QSPR model should greatly consider two aspects: selecting the optimal variable subset from a large pool of molecular descriptors and detecting outliers from a pool of samples.The two problems have the specific similarity and complementarity to some extent.Given a particular learning algorithm on a particular data set,one should consider how the interaction could happen between variable selection and outlier detection.In this paper,we describe a consistent methodology for simultaneously performing variable subset selection and outlier detection using the idea of statistical distribution which can be simulated by the establishment of many cross-predictive linear models.The approach exploits the fact that the distribution of linear model coefficients provides a mechanism for ranking and interpreting the effects of variable,while the distribution of prediction errors provides a mechanism for differentiating the outliers from normal samples.The use of statistic of these distributions,namely mean value and standard deviation,inherently provides a feasible way to effectively describe the information contained by the original samples.Several examples are used to demonstrate the prediction ability of our proposed approach through the comparison of different approaches as well as their combinations.

关键词： QSAR/QSPR outlier detection variable selection Monte Carlo statistical distribution

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An online outlier detection method for process control time series

An online outlier detection method for process control time ...

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2011 Chinese Control and Decision Conference（CCDC）

作者： Liu Fang is with School of Information Science and Engineering Northeastern University,Shenyang,China,Mao Zhi-zhong is with School of Information Science and Engineering Key Laboratory of Integrated Automation of Process Industry,Norheastern University,Ministry of Education,Shenyang,China

The ability to detect outlier online in process control filed is essential in many real-world system analysis applications.Previous algorithms require some " clean" data to construct the statistical model at beginning,which was used to detect outlier.But actually,these clean data can not obtain at all.In this paper,we investigate a machine learning,descriptor-based approach that dose not require clean data to model,based on least square support vector outlier detection.A online window-based learn algorithm is introduced.Theoretical consideration as well as simulations on real process data demonstrate its practical efficiency.

关键词： outlier detection least square support vector process control online detection

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A Novel Subspace outlier detection Approach in High Dimensional Data Sets

A Novel Subspace Outlier Detection Approach in High Dimensio...

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2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Jinsong Leng School of Computer and Security Edith Cowan University

Many real applications are required to detect outliers in high dimensional data sets. The major difficulty of mining outliers lies on the fact that outliers are often embedded in subspaces. No efficient methods are available in general for subspace-based outlier detection. Most existing subspace-based outlier detection methods identify outliers by searching for abnormal sparse density units in subspaces. In this paper, we present a novel approach for finding outliers in the ’interesting’ subspaces. The interesting subspaces are strongly correlated with `good’ clusters. This approach aims to group the meaningful subspaces and then identify outliers in the projected subspaces. In doing so, an extension to the subspace-based clustering algorithm is proposed so as to find the ’good’ subspaces, and then outliers are identified in the projected subspaces using some classical outlier detection techniques such as distance-based and density-based algorithms. Comprehensive case studies are conducted using various types of subspace clustering and outlier detection algorithms. The experimental results demonstrate that the proposed method can detect outliers effectively and efficiently in high dimensional data sets.

关键词： Data Mining Subspace Clustering outlier detection Dimensional Reduction

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A New Support Vector Machine Model for outlier detection

A New Support Vector Machine Model for Outlier Detection

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2011 4th IEEE International Conference on Computer Science and Information Technology(ICCSIT 2011)

作者： Yimin Ai Higher Vocational and Technical College University of Science and Technology Liaoning Anshan Liaoning China Jun Xing School of Science University of Science and Technology Liaoning Anshan Liaoning China

outlier detection has attracted more and more attentions from researchers due to its extensive applications in various fields.In this paper,a new SVM（Support Vector Machine）model is presented for a special type of outlier detection problem,where the majority of training data is normal sample and it also contains a small number of anomaly samples.The experimental results on several datasets show that the proposed algorithm is valid and can achieve better accuracy compared with the published algorithms.

关键词： outlier detection Support Vector Machine Machine Fault detection Machine Learning

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A New outlier detection Algorithm Based on Manifold Learning

A New Outlier Detection Algorithm Based on Manifold Learning

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2010 Chinese Control and Decision Conference

作者： Zhigang Tang~(1,2),Jun Yang~1,Bingru Yang~1 1.School of Information Engineering,University of Science and Technology Beijing,Beijing 100083,China 2.School of Math and Physics,Nanhua University,Hengyang 420003,China

＜正＞Detecting outliers in a large set of data objects is a major data mining task aiming at finding different mechanisms responsible for different groups of objects in a data set.All existing approaches,however,are based on an assessment of distances（sometimes indirectly by assuming certain distributions） in the full-dimensional Euclidean data space.In high-dimensional data,these approaches are bound to deteriorate due to the notorious"curse of dimensionality". In this paper,we propose a novel approach named MLOD（Manifold Learning -Based outlier detection）,This way,the effects of the"curse of dimensionality"are alleviated compared to purely distance-based approaches.A main advantage of our new approach is that our method does not rely on any parameter selection influencing the quality of the achieved ranking.Empirical studies conducted on both real and synthetic data sets show that significant improvements in detection rate and false alarm rate are achieved using the proposed framework.

关键词： Manifold Learning LIE algorithm outlier detection

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A Hybrid Deep Learning-Based Unsupervised Anomaly detection in High Dimensional Data

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Computers, Materials & Continua 2022年第3期70卷 5363-5381页

作者： Amgad Muneer Shakirah Mohd Taib Suliman Mohamed Fati Abdullateef O.Balogun Izzatdin Abdul Aziz Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32160Malaysia Centre for Research in Data Science(CERDAS) Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Information Systems Department Prince Sultan UniversityRiyadh11586Saudi Arabia

Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems.Many issues in this field still unsolved,so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data.Such a phenomenon is referred to as the“curse of dimensionality”that affects traditional techniques in terms of both accuracy and performance.Thus,this research proposed a hybrid model based on Deep Autoencoder Neural Network(DANN)with five layers to reduce the difference between the input and output.The proposed model was applied to a real-world gas turbine(GT)dataset that contains 87620 columns and 56 rows.During the experiment,two issues have been investigated and solved to enhance the results.The first is the dataset class imbalance,which solved using SMOTE technique.The second issue is the poor performance,which can be solved using one of the optimization algorithms.Several optimization algorithms have been investigated and tested,including stochastic gradient descent(SGD),RMSprop,Adam and Adamax.However,Adamax optimization algorithm showed the best results when employed to train theDANNmodel.The experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40%,F1-score of 0.9649,Area Under the Curve(AUC)rate of 0.9649,and a minimal loss function during the hybrid model training.

关键词： Anomaly detection outlier detection unsupervised learning autoencoder deep learning hybrid model

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