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MACHINE LEARNING 2004年第1期54卷 45-66页

作者： Tax, DMJ Duin, RPW Delft Univ Technol Fac Appl Phys Pattern Recognit Grp NL-2628 CJ Delft Netherlands

data domain description concerns the characterization of a data set. A good description covers all target data but includes no superfluous space. The boundary of a dataset can be used to detect novel data or outliers. We will present the support vector data description (SVDD) which is inspired by the support vector Classifier. It obtains a spherically shaped boundary around a dataset and analogous to the support vector Classifier it can be made flexible by using other kernel functions. The method is made robust against outliers in the training set and is capable of tightening the description by using negative examples. We show characteristics of the support vector data descriptions using artificial and real data.

关键词： outlier detection novelty detection one-class classification support vector classifier support vector data description

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Anomaly detection for high-speed machining using hybrid regularized support vector data description

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2025年 94卷

作者： Ma, Zhipeng Zhao, Ming Dai, Xuebin Chen, Yang Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Zhejiang Univ Sch Mech Engn Hangzhou 310027 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Ujoin Tech Shanghai Co Ltd Shanghai 201199 Peoples R China

Process monitoring in high-speed machining (HSM) is essential to guarantee product quality and improve manufacturing efficiency. Nevertheless, the data acquired from practical machining processes are completely unlabeled and severely unbalanced, which may be seriously insufficient to support deep learning-based anomaly detection. Furthermore, the collected signals are inevitably contaminated by environmental noises and uncertain factors. How to remove these disturbances according to data distribution characteristics remains a challenging issue. To tackle these limitations, a novel interpretable machine learning approach, called hybrid regularized support vector data description (H-SVDD), is proposed for unsupervised anomaly detection during HSM. In this work, an adaptive local kernel density estimate is first constructed to eliminate outlier interferences, and assigns interpretable weights to optimize the SVDD for improving detection accuracy. Subsequently, by introducing the l p-norm penalty mechanism, a generalized probability density regularized SVDD is innovatively designed to enhance the descriptive capability for complex machining processes. Finally, a hyperparameter tuning strategy based on Bayesian optimization is developed to improve generalizability and stability. The data collected from CNC machines are used to verify the superiority of the proposed method. Experimental results show that the proposed H-SVDD has higher detection accuracy than current SVDD methods and eliminates false alarms caused by noise interferences. This work may provide a useful solution for independently perceiving the health conditions of HSM.

关键词： Anomaly detection High-speed machining Machine learning Probability density estimation support vector data description

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Boundary sample extraction support vector data description: a novel anomaly detection method for large-scale data

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第2期36卷 026012-026012页

作者： Wang, Xiaofei Chen, Yongzhan Xu, Fenglei Gao, Yanli Wang, Yuanxin Qiao, Yuchuan Dai, Haomin Wang, Qiang Naval Aeronaut Univ Dept Control Sci & Engn Qingdao Branch Qingdao 266041 Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai 200433 Peoples R China

support vector data description (SVDD) has been effectively used in many anomaly detection problems. Equipped with kernel functions, its training complexity grows exponentially with the increase in training data, which makes it less practical for large-scale datasets. In this paper, we propose a boundary sample extraction SVDD (BSE-SVDD) anomaly detection method based on data reduction, aiming for use with large-scale data. Firstly, the BSE mechanism is established on the basis of demonstrating that the spatial position of the sample is related to its corresponding Lagrange multiplier. Then, the BSE mechanism is used to search for the local optimal solution of the Lagrange multiplier, and all the training samples are sorted. Finally, the top p of ranked samples are extracted as boundary samples for training, while most of the training samples that may be non-support vectors are removed. Compared with SVDD studies based on data reduction, the experimental results on large-scale datasets show that BSE-SVDD can obtain comparable classification accuracy with greatly improved training speed.

关键词： support vector data description boundary samples extraction data reduction stochastic gradient descent large-scale data

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support vector data description model to map urban extent from National Polar-Orbiting Partnership Satellite-Visible Infrared Imaging Radiometer Suite nightlights and normalized difference vegetation index

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JOURNAL OF APPLIED REMOTE SENSING 2016年第2期10卷 026012-1-026012-20页

作者： Zhang, Jinshui Zhou, Zhongwei Shuai, Guanyuan Liu, Hongli Beijing Normal Univ State Key Lab Earth Surface Processes & Resource Beijing 100875 Peoples R China Beijing Normal Univ Coll Resources Sci & Technol Beijing 100875 Peoples R China Michigan State Univ Dept Geol Sci E Lansing MI 48824 USA

We explored a one-class classifier, the support vector data description (SVDD), using the Suomi National Polar-Orbiting Partnership Satellite-Visible Infrared Imaging Radiometer Suite and normalized difference vegetation index to map the urban extent, which was tested in the Beijing and Tianjin city group area. The urban edge-pixels were selected as training samples for SVDD based on a profile-based sampling method combining nighttime light value histograms. The results showed that the overall accuracy of SVDD was similar to the support vector machine (SVM) model. However, kappa coefficients of SVDD for highly developed cities were superior to SVM, as producer and user accuracies of SVDD were almost equal to show high agreement of urban and nonurban areas. For metropolitan areas, such as Beijing and Tianjin, the urban extent generated by SVDD is closer to the reference data. The R-2 between the quantity of SVDD-estimated urban extent and population, 0.86, was higher than that obtained from SVM, 0.76, indicating that the estimated urban extent from the SVDD is more efficient for understanding the population development. The SVDD was further applied for three other representative metropolitans in China: Shanghai, Guangzhou, and Shenzhen to validate the SVDD's performance, and similar results were achieved. The success of the SVDD-based urban extent extraction improves our ability to map urban extent at regional and national scales. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： Suomi Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite edge-pixels simulated annealing support vector data description urban extent

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support vector data description using privileged information

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ELECTRONICS LETTERS 2015年第14期51卷 1075-+页

作者： Zhang, Wenbo Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

support vector data description (SVDD) is a data description method which gives the target data set a hypersphere-shaped description and can be used for one-class classification or outlier detection. To further improve its performance, a novel SVDD called SVDD+ which introduces the privileged information to the traditional SVDD is proposed. This privileged information, which is ignored by the classical SVDD but often exists in human learning, will optimise the training phase by constructing a set of correcting functions. The performance of SVDD+ on data sets from the UCI machine learning repository and radar emitter recognition is demonstrated. The experimental results indicate the validity and advantage of this method.

关键词： support vector machines data description learning (artificial intelligence) pattern classification set theory support vector data description privileged information SVDD hypersphere-shaped description one-class classification outlier detection UCI machine learning repository radar emitter recognition

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support vector data description for detecting the air-ground interface in ground penetrating radar signals

Support vector data description for detecting the air-ground...

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Conference on Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI

作者： Wood, Joshua Wilson, Joseph Univ Florida CISE Gainesville FL 32611 USA

ISBN: (纸本)9780819485915

In using GPR images for landmine detection it is often useful to identify the air-ground interface in the GRP signal for alignment purposes. A common simple technique for doing this is to assume that the highest return in an A-scan is from the reflection due to the ground and to use that as the location of the interface. However there are many situations, such as the presence of nose clutter or shallow sub-surface objects, that can cause the global maximum estimate to be incorrect. A support vector data description (SVDD) is a one-class classifier related to the SVM which encloses the class in a hyper-sphere as opposed to using a hyper-plane as a decision boundary. We apply SVDD to the problem of detection of the air-ground interface by treating each sample in an A-scan, with some number of leading and trailing samples, as a feature vector. Training is done using a set of feature vectors based on known interfaces and detection is done by creating feature vectors from each of the samples in an A-scan, applying the trained SVDD to them and selecting the one with the least distance from the center of the hyper-sphere. We compare this approach with the global maximum approach, examining both the performance on human truthed data and how each method affects false alarm and true positive rates when used as the alignment method in mine detection algorithms.

关键词： ground penetrating radar landmine detection ground finding support vector data description

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support vector data description based Multivariate Cumulative Sum Control Chart

Support Vector Data Description based Multivariate Cumulativ...

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International Conference on Advanced Design and Manufacturing Engineering (ADME 2011)

作者： He, Shuguang Zhang, Chunyan Tianjin Univ Sch Management Tianjin 300072 Peoples R China

ISBN: (纸本)9783037852156

A SVDD (support vector data description) based MCUSUM (Multivariate Cumulative Sum) chart is proposed and referred as S-MCUSUM chart, which has an advantage of distribution free. Numerical experiments on the performance of the S-MCUSUM chart is compared to the COT (Cumulative of T) chart. The results show that the COT chart is somewhat better than the S-MCUSUM chart for multivariate normally distributed data. However, the S-MCUSUM chart is much better than the COT chart for banana-shaped distributed data which is a typical non-normal distribution.

关键词： support vector data description Cumulative sum chart Multivariate statistical process control One class classification

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A fast iterative algorithm for support vector data description

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2019年第5期10卷 1173-1187页

作者： Zheng, Songfeng Missouri State Univ Dept Math Springfield MO 65897 USA

support vector data description (SVDD) is a well known model for pattern analysis when only positive examples are reliable. SVDD is usually trained by solving a quadratic programming problem, which is time consuming. This paper formulates the Lagrangian of a simply modified SVDD model as a differentiable convex function over the nonnegative orthant. The resulting minimization problem can be solved by a simple iterative algorithm. The proposed algorithm is easy to implement, without requiring any particular optimization toolbox. Theoretical and experimental analysis show that the algorithm converges r-linearly to the unique minimum point. Extensive experiments on pattern classification were conducted, and compared to the quadratic programming based SVDD (QP-SVDD), the proposed approach is much more computationally efficient (hundreds of times faster) and yields similar performance in terms of receiver operating characteristic curve. Furthermore, the proposed method and QP-SVDD extract almost the same set of support vectors.

关键词： support vector data description Quadratic programming Penalty function method Lagrangian dual function support vectors

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A novel framework based on two-stage multi-view feature optimization and improved support vector data description for aeroengine bearing early fault detection

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 245卷

作者： Hou, Zhaoguo Wang, Huawei Yue, Yubin Xiong, Minglan Zhang, Wenxuan Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 211106 Peoples R China

Early fault detection is crucial to avoid catastrophic flight accidents caused by unplanned downtime of equipment. Aimed at the shortcomings of feature selection and early fault detection, this paper proposes a method combining two -stage multi -view feature optimization and improved support vector data description for aeroengine bearing early fault detection. First, a comprehensive evaluation index is constructed via combining monotonicity, correlation, robustness index and entropy weight method to be used for initial screening sensitive degradation features from multi -view candidate degradation feature. After that, the hierarchical clustering algorithm and local preserving projection algorithm is introduced to realize sensitive degradation features clustering and dimensionality reduction. On this basis, the Euclidean distance sample reduction and the sparrow search algorithm is utilized to complete the training sample selection and model parameter optimization of different clusters and further use the verification samples to filter the optimal features and model. Finally, based on the optimal degradation features and models, a fusion performance degradation index is built to achieve intelligent detection of early fault. Comparisons with some existing fault detection methods, the proposed method improves degradation trend sensibility and demonstrates potential of good early fault detection engineering applications.

关键词： Early fault detection Aeroengine bearing Two-stage feature optimization support vector data description Sample reduction Sparrow search algorithm

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Kernel parameter variation-based selective ensemble support vector data description for oil spill detection on the ocean via hyperspectral imaging

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JOURNAL OF APPLIED REMOTE SENSING 2017年第3期11卷

作者： Uslu, Faruk Sukru Yildiz Tech Univ Elect & Commun Engn Dept Istanbul Turkey

Oil spills on the ocean surface cause serious environmental, political, and economic problems. Therefore, these catastrophic threats to marine ecosystems require detection and monitoring. Hyperspectral sensors are powerful optical sensors used for oil spill detection with the help of detailed spectral information of materials. However, huge amounts of data in hyperspectral imaging (HSI) require fast and accurate computation methods for detection problems. support vector data description (SVDD) is one of the most suitable methods for detection, especially for large data sets. Nevertheless, the selection of kernel parameters is one of the main problems in SVDD. This paper presents a method, inspired by ensemble learning, for improving performance of SVDD without tuning its kernel parameters. Additionally, a classifier selection technique is proposed to get more gain. The proposed approach also aims to solve the small sample size problem, which is very important for processing high-dimensional data in HSI. The algorithm is applied to two HSI data sets for detection problems. In the first HSI data set, various targets are detected;in the second HSI data set, oil spill detection in situ is realized. The experimental results demonstrate the feasibility and performance improvement of the proposed algorithm for oil spill detection problems.

关键词： ensemble method hyperspectral imaging ocean remote sensing kernel parameters oil spill detection support vector data description

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