The Experts below are selected from a list of 14193 Experts worldwide ranked by ideXlab platform
Moncef Gabbouj - One of the best experts on this subject based on the ideXlab platform.
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Ellipsoidal Subspace Support Vector Data Description
IEEE Access, 2020Co-Authors: Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis, Moncef GabboujAbstract:In this paper, we propose a novel method for transforming Data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms Data into a new subspace optimized for ellipsoidal encapsulation of target class Data. We provide both linear and non-linear formulations for the proposed method. The method takes into account the covariance of the Data in the subspace; hence, it yields a more generalized solution as compared to Subspace Support Vector Data Description for a hypersphere. We propose different regularization terms expressing the class variance in the projected space. We compare the results with classic and recently proposed one-class classification methods and achieve better results in the majority of cases. The proposed method is also noticed to converge much faster than recently proposed Subspace Support Vector Data Description.
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Subspace Support Vector Data Description
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Fahad Sohrab, Jenni Raitoharju, Moncef Gabbouj, Alexandros IosifidisAbstract:This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the Data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the Data mapping along with Data description in order to define a compact class representation in a low-dimensional feature space. We provide both linear and non-linear mappings for the proposed method. Experiments on 14 publicly available Datasets indicate that the proposed Subspace Support Vector Data Description provides better performance compared to baselines and other recently proposed one-class classification methods.
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ICPR - Subspace Support Vector Data Description
2018 24th International Conference on Pattern Recognition (ICPR), 2018Co-Authors: Fahad Sohrab, Jenni Raitoharju, Moncef Gabbouj, Alexandros IosifidisAbstract:This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the Data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the Data mapping along with Data description in order to define a compact class representation in a low-dimensional feature space. We provide both linear and non-linear mappings for the proposed method. Experiments on 14 publicly available Datasets indicate that the proposed Subspace Support Vector Data Description provides better performance compared to baselines and other recently proposed one-class classification methods.
Robert P.w. Duin - One of the best experts on this subject based on the ideXlab platform.
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Support Vector Data Description
Machine Learning, 2004Co-Authors: Robert P.w. DuinAbstract: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.
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Feature Scaling in Support Vector Data Descriptions
2000Co-Authors: David M. J. Tax, Robert P.w. DuinAbstract:In previous research the Support Vector Data Description is proposed to solve the problem of One-Class classification. In One-Class classification one set of Data, called the target set, has to be distinguished from the rest of the feature space. This description should be constructed such that objects not originating from the target set, by definition the outlier class, are not accepted by the Data description. In this paper the Support Vector Data Description is applied to the problem of image Database retrieval. The user selects an example image region as target class and resembling images from a Database should be retrieved. This application shows some of the weaknesses of the SVDD, particularly the dependence on the scaling of the features. By rescaling features and combining several descriptions oll well scaled feature sets, performance can be significantly improved.
Ning Jing - One of the best experts on this subject based on the ideXlab platform.
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HiVision: Rapid visualization of large-scale spatial Vector Data
Computers & Geosciences, 2021Co-Authors: Xue Ouyang, Luo Chen, Ning JingAbstract:Abstract Rapid visualization of large-scale spatial Vector Data is a long-standing challenge in Geographic Information Science. In existing methods, the computation overheads grow rapidly with Data volumes, leading to the incapability of providing real-time visualization for large-scale spatial Vector Data, even with parallel acceleration technologies. To fill the gap, we present HiVision, a display-driven visualization model for large-scale spatial Vector Data. Different from traditional Data-driven methods, the computing units in HiVision are pixels rather than spatial objects to achieve real-time performance, and efficient spatial-index-based strategies are introduced to estimate the topological relationships between pixels and spatial objects. HiVision can maintain exceedingly good performance regardless of the Data volume due to the stable pixel number for display. In addition, an optimized parallel computing architecture is proposed in HiVision to ensure the ability of real-time visualization. Experiments show that our approach outperforms traditional methods in rendering speed and visual effects while dealing with large-scale spatial Vector Data, and can provide interactive visualization of Datasets with billion-scale points/segments/edges in real-time with flexible rendering styles. The HiVision code is open-sourced at https://github.com/MemoryMmy/HiVision with an online demonstration.
Fahad Sohrab - One of the best experts on this subject based on the ideXlab platform.
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Ellipsoidal Subspace Support Vector Data Description
IEEE Access, 2020Co-Authors: Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis, Moncef GabboujAbstract:In this paper, we propose a novel method for transforming Data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms Data into a new subspace optimized for ellipsoidal encapsulation of target class Data. We provide both linear and non-linear formulations for the proposed method. The method takes into account the covariance of the Data in the subspace; hence, it yields a more generalized solution as compared to Subspace Support Vector Data Description for a hypersphere. We propose different regularization terms expressing the class variance in the projected space. We compare the results with classic and recently proposed one-class classification methods and achieve better results in the majority of cases. The proposed method is also noticed to converge much faster than recently proposed Subspace Support Vector Data Description.
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Subspace Support Vector Data Description
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Fahad Sohrab, Jenni Raitoharju, Moncef Gabbouj, Alexandros IosifidisAbstract:This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the Data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the Data mapping along with Data description in order to define a compact class representation in a low-dimensional feature space. We provide both linear and non-linear mappings for the proposed method. Experiments on 14 publicly available Datasets indicate that the proposed Subspace Support Vector Data Description provides better performance compared to baselines and other recently proposed one-class classification methods.
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ICPR - Subspace Support Vector Data Description
2018 24th International Conference on Pattern Recognition (ICPR), 2018Co-Authors: Fahad Sohrab, Jenni Raitoharju, Moncef Gabbouj, Alexandros IosifidisAbstract:This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the Data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the Data mapping along with Data description in order to define a compact class representation in a low-dimensional feature space. We provide both linear and non-linear mappings for the proposed method. Experiments on 14 publicly available Datasets indicate that the proposed Subspace Support Vector Data Description provides better performance compared to baselines and other recently proposed one-class classification methods.
Alexandros Iosifidis - One of the best experts on this subject based on the ideXlab platform.
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Ellipsoidal Subspace Support Vector Data Description
IEEE Access, 2020Co-Authors: Fahad Sohrab, Jenni Raitoharju, Alexandros Iosifidis, Moncef GabboujAbstract:In this paper, we propose a novel method for transforming Data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms Data into a new subspace optimized for ellipsoidal encapsulation of target class Data. We provide both linear and non-linear formulations for the proposed method. The method takes into account the covariance of the Data in the subspace; hence, it yields a more generalized solution as compared to Subspace Support Vector Data Description for a hypersphere. We propose different regularization terms expressing the class variance in the projected space. We compare the results with classic and recently proposed one-class classification methods and achieve better results in the majority of cases. The proposed method is also noticed to converge much faster than recently proposed Subspace Support Vector Data Description.
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Subspace Support Vector Data Description
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Fahad Sohrab, Jenni Raitoharju, Moncef Gabbouj, Alexandros IosifidisAbstract:This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the Data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the Data mapping along with Data description in order to define a compact class representation in a low-dimensional feature space. We provide both linear and non-linear mappings for the proposed method. Experiments on 14 publicly available Datasets indicate that the proposed Subspace Support Vector Data Description provides better performance compared to baselines and other recently proposed one-class classification methods.
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ICPR - Subspace Support Vector Data Description
2018 24th International Conference on Pattern Recognition (ICPR), 2018Co-Authors: Fahad Sohrab, Jenni Raitoharju, Moncef Gabbouj, Alexandros IosifidisAbstract:This paper proposes a novel method for solving one-class classification problems. The proposed approach, namely Subspace Support Vector Data Description, maps the Data to a subspace that is optimized for one-class classification. In that feature space, the optimal hypersphere enclosing the target class is then determined. The method iteratively optimizes the Data mapping along with Data description in order to define a compact class representation in a low-dimensional feature space. We provide both linear and non-linear mappings for the proposed method. Experiments on 14 publicly available Datasets indicate that the proposed Subspace Support Vector Data Description provides better performance compared to baselines and other recently proposed one-class classification methods.
