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DASVDD: Deep Autoencoding support vector data descriptor for Anomaly Detection

IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2024年第8期36卷 3739-3750页

作者： Hojjati, Hadi Armanfard, Narges McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0C3 Canada Mila Quebec AI Inst QC H2S Montreal PQ Canada

One-Class anomaly detection aims to detect anomalies from normal samples using a model trained on normal data. With recent advancements in deep learning, researchers have designed efficient one-class anomaly detection methods. Existing works commonly use neural networks to map the data into a more informative representation and then apply an anomaly detection algorithm. In this paper, we propose a method, DASVDD, that jointly learns the parameters of an autoencoder while minimizing the volume of an enclosing hypersphere on its latent representation. We propose a novel anomaly score that combines the autoencoder's reconstruction error and the distance from the center of the enclosing hypersphere in the latent representation. Minimizing this anomaly score aids us in learning the underlying distribution of the normal class during training. Including the reconstruction error in the anomaly score ensures that DASVDD does not suffer from the hypersphere collapse issue since the DASVDD model does not converge to the trivial solution of mapping all inputs to a constant point in the latent representation. Experimental evaluations on several benchmark datasets show that the proposed method outperforms the commonly used state-of-the-art anomaly detection algorithms while maintaining robust performance across different anomaly classes.

关键词： Anomaly detection deep autoencoder deep learning support vector data descriptor deep autoencoder deep learning support vector data descriptor

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Creating and measuring diversity in multiple classifier systems using support vector data description

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APPLIED SOFT COMPUTING 2011年第8期11卷 4931-4942页

作者： Haghighi, Mehdi Salkhordeh Vahedian, Abedin Yazdi, Hadi Sadoghi Ferdowsi Univ Mashhad Dept Comp Engn Mashhad Iran

In this paper, a new method is introduced to construct Multiple Classifier Systems (MCSs). It is based on controlling diversity among base classifiers according to a new method in measuring diversity in kernel space. The method admits a tradeoff between individual classifier and multiple classifier accuracy and diversity as each base classifier requires knowledge of the choices made by the other MCS members. This knowledge is included in the method using data descriptors as a tool for creating diversity between base classifiers in kernel space. data description properties are also used for measuring diversity. A new combining method presented in this paper completes this work. Performance of the proposed method is evaluated on a number of known benchmark datasets. Analyzing the results shows that the proposed method improves system's overall performance and accuracy in many cases. It also measures diversity more precisely. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Multiple classifier system Classifier ensemble support vector data descriptor MCS diversity Classifier fusion

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Mapping specific habitats from remotely sensed imagery: support vector machine and support vector data description based classification of coastal saltmarsh habitats

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ECOLOGICAL INFORMATICS 2007年第2期2卷 83-88页

作者： Sanchez-Hernandez, Carolina Boyd, Doreen S. Foody, Giles M. Univ Nottingham Sch Geog Nottingham NG7 2RD England Ordnance Survey Res & Innovat Southampton SO16 4GU Hants England

Remote sensing has considerable potential for the provision of information on the distribution of habitats that may be used to inform a variety of activities such as those required through the European Union's Habitats Directive. Such programmes are often resource-limited with a need for innovative methods that optimise resource use. This paper explores two approaches to resource savings when mapping habitats from remotely sensed imagery. The first approach realises that in an area of study often interest is focused on a specific habitat with the remaining land cover classes in the region of no importance. In such circumstances conventional statistical supervised classification analyses may be inefficient and yield a map of sub-optimal accuracy. The second approach seeks to further reduce the training requirements of a supervised classification. For this support vector machine (SVM) based approaches to classification are explored to map coastal saltmarsh habitats in North Norfolk, UK from a Landsat Enhanced Thematic Mapper (ETM+) image. A series of classifications using SVM based approaches and the Maximum Likelihood classifier (MLC) were undertaken. Classification accuracies were significantly higher using the SVM based approaches (e.g., 92.0% overall accuracy) than the MLC (64.8% overall accuracy). The SVM based classifications were demonstrated to be attractive for mapping a priority habitat in that the focus is on the habitat of interest to be mapped throughout the classification process resulting in a reduced need for training data. Moreover, it was shown that this can be further optimised through the use of intelligent training. This approach, based on the use of the support vector data description (SVDD), saved resource requirements even further in that training data were required only for the class of interest and yet still obtained high classification accuracies (95.2% overall accuracy). The wider adoption of SVM based classification of remotely sensed imag

关键词： remote sensing habitat saltmash one-class classification support vector machines support vector data descriptor

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Text Segmentation in Ancient Topographic Maps and Floor Plans with support vector data Description

Text Segmentation in Ancient Topographic Maps and Floor Plan...

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International Joint Conference on Neural Networks (IJCNN)

作者： Machado, S. C. S. Mello, C. A. B. Univ Fed Pernambuco Ctr Informat Recife PE Brazil

ISBN: (纸本)9781479919598

Images of ancient maps and floor plans can present a great challenge for common character recognition tools. Besides the damage caused by time and handling, these documents have an important part of their information described graphically. In most examples, drawings of rivers or walls occupy most part of the document. Usually, text has different styles, sizes and orientations with possible overlapping with graphics. This paper presents a new method for text segmentation in images of ancient topographic maps and floor plans that uses a machine learning algorithm specialized in novelty detection to decide which components of the image are textual. Despite using artificial text examples for training, the method is able to outperform other state-of-the-art methods when applied to real images.

关键词： text segmentation topographic maps floor plans support vector data descriptor

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Health Monitoring Framework for Electric Vehicle Drive Train in Digital Twin 25

Health Monitoring Framework for Electric Vehicle Drive Train...

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25th European Conference on Power Electronics and Applications (EPE ECCE Europe)

作者： Kurukuru, Varaha Satya Bharath Khan, Mohammed Ali Singh, Rupam Silicon Austria Labs GmbH Europastr 12 Villach Austria Univ Southern Denmark Alsion 2 Sonderborg Denmark Univ Southern Denmark Campusvej 55 Odense Denmark

ISBN: (纸本)9789075815412

As electric vehicles (EVs) continue to evolve and become more intricate, it becomes increasingly important to monitor their health continuously to ensure both safe operation and optimal performance. To address this need, this research paper proposes a comprehensive health monitoring framework that leverages the concept of Digital Twin (DT). The DT incorporates a bond graph (BG) model, which accurately represents the intricate structure and functionality of the EV drivetrain. Additionally, the framework utilizes support vector data Description (SVDD) to train and classify measured data effectively, enabling efficient fault detection and diagnosis. By integrating the developed BG model and SVDD into the digital twin, the framework enables real-time monitoring and predictive analysis of the EV's health status. The simulation results demonstrate the effectiveness of this framework, showcasing high accuracies of 98.7% during training and 96.21% during testing. These results validate the potential of the proposed approach to ensure the reliable and efficient operation of EVs while also minimizing the risk of malfunctions and ensuring a safe driving experience for users.

关键词： Electric vehicles (EVs) Drive train Health Monitoring (HM) Bond graph (BG) support vector data descriptor Digital twin

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Mapping Burned Areas from Landsat TM Imags: a Comparative Study

Mapping Burned Areas from Landsat TM Imags: a Comparative St...

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International Conference on Computer Vision in Remote Sensing (CVRS)

作者： Mazher, Abeer Li, Peijun Zhang, Jun Peking Univ Inst Remote Sensing Beijing 100871 Peoples R China

ISBN: (纸本)9781467312745;9781467312721

Remote sensing is a major source of mapping the burned area caused by forest fire. The focus in this application is to map a single class of interest, i.e. burned area. In this study, three different data combinations were classified using different classifiers and quantitatively compared. The adopted classifiers are the support vector data descriptor (SVDD), a one-class classifier, the Binary support vector Machines (SVMs) classifier and the traditional Maximum Likelihood classifier (ML). At first, the Principal Component Analysis (PCA) was applied to extract the best possible features form the original multispectral image (OMI) and calculated spectral indices (SI). Then the resulting subset of features was applied in the classification process. The comparative study has undertaken to find firstly, the best possible set of features (data combination) and secondly, an effective classifier to map the burned areas. Experimental results indicate that the best possible set of features was attained by data combination-III (i.e., combining SI and OMI information) in the study area. Furthermore, the results of the SVM showed the higher classification accuracies than the ML, but approximately equivalent to the SVDD, which only requires training samples from the class of interest. Experimental results also demonstrate that even though the SVDD for mapping the burned areas doesn't showed the higher classification accuracy than the SVM, but it shows the suitability for the cases with few or poorly represented labeled samples available. The parameters should be further optimized through the use of intelligent training for improving the accuracy of the SVDD.

关键词： Feature extraction Principal components Vegetation indices One-class classification support vector machines support vector data descriptor

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Text Segmentation in Ancient Topographic Maps and Floor Plans with support vector data Description

Text Segmentation in Ancient Topographic Maps and Floor Plan...

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International Joint Conference on Neural Networks

作者： S.C.S. Machado C.A.B. Mello Centro de Informática Universidade Federal de Pernambuco Recife Brazil

ISBN: (纸本)9781479919611

关键词： text segmentation topographic maps floor plans support vector data descriptor

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Mapping Burned Areas from Landsat TM Imags:a Comparative Study

Mapping Burned Areas from Landsat TM Imags:a Comparative Stu...

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2012年遥感中的计算机视觉国际会议

作者： Abeer Mazher Peijun Li Jun Zhang Institute of Remote Sensing and GIS Peking University Beijing 100871 China

Remote sensing is a major source of mapping the burned area caused by forest *** focus in this application is to map a single class of interest,*** *** this study,three different data combinations were classified using different classifiers and quantitatively *** adopted classifiers are the support vector data descriptor (SVDD),a one-class classifier,the Binary support vector Machines (SVMs) classifier and the traditional Maximum Likelihood classifier (ML).At first,the Principal Component Analysis (PCA) was applied to extract the best possible features form the original multispectral image (OMI) and calculated spectral indices (SI).Then the resulting subset of features was applied in the classification *** comparative study has undertaken to find firstly,the best possible set of features (data combination) and secondly,an effective classifier to map the burned *** results indicate that the best possible set of features was attained by data combination- III (i.e.,combining SI and OMI information) in the study ***,the results of the SVM showed the higher classification accuracies than the ML,but approximately equivalent to the SVDD,which only requires training samples from the class of *** results also demonstrate that even though the SVDD for mapping the burned areas doesn't showed the higher classification accuracy than the SVM,but it shows the suitability for the cases with few or poorly represented labeled samples *** parameters should be further optimized through the use of intelligent training for improving the accuracy of the SVDD.

关键词： Feature extraction Principal components Vegetation indices , One-class classification support vector machines support vector data descriptor

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