2008
Abstract. 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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Chandan Srivastava · 2011
Cited by 2
David M. J. Tax, Robert P. W. Duin · Machine Learning · 2003
Cited by 3,423Open access
Zhe Wang, Daqi Gao · 2010
Support Vector Data Description (SVDD) was designed to construct a minimum hypersphere so as to enclose all the data of the target class in the one-class classification case. In this paper, we propose a novel Discriminant Support Vector Data Description (DSVDD). The proposed DSVDD adopts the relevant metric learning instead of the original Euclidean distance metric learning in SVDD, where the relevant metric learning can consider the relationship between data. Here through incorporating both the positive and negative equivalence information, the presented DSVDD assigns large weights to the relevant features and tightens the similar data. More importantly, we introduce the discriminant knowledge prior into the proposed algorithm due to considering the negative equivalence information. The experiments show that the proposed DSVDD can bring more accurate classification performance than the conventional SVDD for all the tested data.
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Fahad Sohrab, Jenni Raitoharju, Moncef Gabbouj et al. · arXiv (Cornell University) · 2018
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.
Cited by 4Open access
Guang-Xin Huang, Hua-Fu Chen, Feng Yin · 2010
Support vector data description is a data description method which can give the target data set a spherically shaped description. However, when the target data set contains two classes of objects, this data description method can treat the target data set as a whole and give only one description for the training data set. This paper presents an improved support vector data description, which could give each class of target objects in the target data set a hyper-spherically shaped boundary, if the target data set contains two classes of objects.
Cited by 4
Nguyen, Thanh Phuoc, Tran, Dat, Huang, Xu et al. · Own your potential (DEAKIN) · 2013
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Xiaoming Wang, Shitong Wang, Hong Peng · Journal of Computer Applications · 2013
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Hong-Jie Xing, Pingping Zhang · Pattern Recognition · 2023
Cited by 12
Lyulei Zhang · 2014
Based on the concept of relative density degrees,a density-punished support vector data description method is presented.The relative density degrees are associated with punishing misclassifications.If the relative density degree of the sample is large,it is likely to be a target sample.Thus,a large penalty should be put on its misclassification.Similarly,if the relative density degree of the sample is small,it might be a boundary or noise point so that the corresponding penalty for its misclassification should be small as well.The experimental results on UCI datasets show that the proposed method has better performance compared with support vector data description and density-induced support vector data description.
Cited by 2
Jorge Guevara, Stephane Canu, Roberto Hirata · 2016
This work shows how to estimate the support of the distribution of some data when observations in the data have uncertainties. To model uncertainties, we consider each observation of the training set to be a random vector distributed according to a distribution with first and second moments in a local vicinity. To estimate the support, we used the support vector data description method. Chance Constrain Approach • Let {xi ∼ (x̂i,Σi)}ni=1 be the training set, the probabilistic levels
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Yanshan Xiao, Bo Liu, Longbing Cao et al. · 2009
SVDD has been proved a powerful tool for outlier de-tection. However, in detecting outliers on multi-distribution data, namely there are distinctive distributions in the data, it is very challenging for SVDD to generate a hyper-sphere for distinguishing outliers from normal data. Even if such a hyper-sphere can be identified, its performance is usu-ally not good enough. This paper proposes an multi-sphere SVDD approach, named MS-SVDD, for outlier detection on multi-distribution data. First, an adaptive sphere detec-tion method is proposed to detect data distributions in the dataset. The data is partitioned in terms of the identified data distributions, and the corresponding SVDD classifiers are constructed separately. Substantial experiments on both artificial and real-world datasets have demonstrated that the proposed approach outperforms original SVDD.1 1
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Taha J. Alhindi, Omar Alturkistani, Jaeseung Baek et al. · Pattern Recognition · 2026
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P. Juszczak, D. M. J. Tax, R. P. W. Duin · 2008
When in a classification problem only samples of one class are easily accessible, this problem is called a one-class classification problem. Many standard classifiers, like backpropagation neural networks, fail on this data. Some other classifiers, like k-means clustering or nearest neighbor classifier can be applied after some minor changes. In this paper we focus on the support vector data description classifier, which is especially constructed for one-class classification. But this method appears to be sensitive to scaling of the individual features of the dataset. We show that it is possible to improve its performance by adequate scaling of the feature space. Some results will be shown on artificial dataset and handwritten digits dataset. 1
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Chandan Gautam, Aruna Tiwari · 2018
In the last decade, SVM-based one-class classifier is well explored for anomaly detection using single-kernel learning. Recently, it is also explored for multiple-kernel learning using a fixed combination of weight where same weight is assigned to each kernel over the complete input space. In this paper, an SVM-based one-class classifier (i.e. Support Vector Data Descriptor (SVDD)) is adapted for localized multi-kernel learning and referred to LMSVDD. Here, the present locality in the input space is considered as the deciding factor to assign different weights to a kernel for different regions of the input space. This localization has been achieved by using a gating function which is trained in tandem with an SVDD-based one-class classifier. Localization also helps in achieving the sparser solution compared to existing multi-kernel-based method as it uses less number of support vectors in many cases. The performance is evaluated based on the extensive experiment over 23 benchmark datasets from various disciplines and compared LMSVDD with 6 state-of-the-art kernel-based methods. LMSVDD outperformed existing single and multi-kernel based methods and the results have been also statistically verified using a Friedman test.
Cited by 4
Sangwook Kim, Yong-Hwa Choi, Minho Lee · Neurocomputing · 2015
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Trung Le, Dat Tran, Wanli Ma · Lecture notes in computer science · 2013
Cited by 6
Edgard M. Maboudou‐Tchao, Emil Agbemade, Jain Chung · Journal of Data Science · 2025
Significant attention has been drawn to support vector data description (SVDD) due to its exceptional performance in one-class classification and novelty detection tasks. Nevertheless, all slack variables are assigned the same weight during the modeling process. This can lead to a decline in learning performance if the training data contains erroneous observations or outliers. In this study, an extended SVDD model, Rescale Hinge Loss Support Vector Data Description (RSVDD) is introduced to strengthen the resistance of the SVDD to anomalies. This is achieved by redefining the initial optimization problem of SVDD using a hinge loss function that has been rescaled. As this loss function can increase the significance of samples that are more likely to represent the target class while decreasing the impact of samples that are more likely to represent anomalies, it can be considered one of the variants of weighted SVDD. To efficiently address the optimization challenge associated with the proposed model, the half-quadratic optimization method was utilized to generate a dynamic optimization algorithm. Experimental findings on a synthetic and breast cancer data set are presented to illustrate the new proposed method’s performance superiority over the already existing methods for the settings considered.
Cited by 1Open access
David M. J. Tax, Robert P. W. Duin · 2015
In previous research the Support Vector Data Descrip-tion is proposed to solve the problem of One-Class clas-sification. 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 ac-cepted by the data description. In this paper the Sup-port Vector Data Description is applied to the problem of image database retrieval. The user selects an exam-ple image region as target class and resembling images from a database should be retrieved. This application shows some of the weaknesses of the SVDD, particu-larly the dependence on the scaling of the features. By rescaling features and combining several descriptions oll well scaled feature sets, performance can be signifi-cantly improved.
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Hui Xie, Stefan Uhlmann, Serkan Kıranyaz et al. · 2014
Due to the simplicity and firm mathematical foundation, Support Vector Machines (SVMs) have been intensively used to solve classification problems. However, training SVMs on real world large-scale databases is computationally costly and sometimes infeasible when the dataset size is massive and non-stationary. In this paper, we propose an incremental learning approach that greatly reduces the time consumption and memory usage for training SVMs. The proposed method is fully dynamic, which stores only a small fraction of previous training examples whereas the rest can be discarded. It can further handle unseen labels in new training batches. The classification experiments show that the proposed method achieves the same level of classification accuracy as batch learning while the computational cost is significantly reduced, and it can outperform other incremental SVM approaches for the new class problem.
Cited by 8