Active Learning Based Support Vector Data Description for Large Data Set Novelty Detection

Face detection is a crucial prestage for face recognition and is often treated as a binary (face and nonface) classification problem. While this strategy is simple to implement, face detection accuracy would drop when nonface training patterns are undersampled. To avoid these problems, we propose in this paper a one-class learning-based face detector called support vector data description (SVDD) committee, which consists of several SVDD members, each of which is trained on a subset of face patterns. Nonfaces are not required in the training of the SVDD committee. Therefore, the face detection accuracy of SVDD committee is independent of the nonface training patterns. Moreover, the proposed SVDD committee is also able to improve generalization ability of the original SVDD when the face data set has a multicluster distribution. Experiments carried out on the extended MIT face data set show that the proposed SVDD committee can achieve better face detection accuracy than the widely used SVM face detector and performs better than other one-class classifiers, including the original SVDD and the kernel principal component analysis (Kernel PCA).

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Anomaly Detection for Hyperspectral Imagery Based on Active Learning with Support Vector Data Description

INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences ◽

10.4156/aiss.vol5.issue12.18 ◽

2013 ◽

Vol 5 (12) ◽

pp. 150-157

Author(s):

Liyan Zhang ◽

Shouyin He ◽

Xianling Zeng ◽

Yonghua Sun ◽

Ronghua Hu

Keyword(s):

Active Learning ◽

Anomaly Detection ◽

Hyperspectral Imagery ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description

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Using a dynamically selective support vector data description model to discover novelties in the control system of electric arc furnace

Measurement and Control ◽

10.1177/0020294020932338 ◽

2020 ◽

Vol 53 (7-8) ◽

pp. 1049-1058

Author(s):

Jiong Zhang ◽

Yue Wang ◽

Qian Li ◽

Biao Wang

Keyword(s):

Novelty Detection ◽

Probabilistic Method ◽

Electric Arc Furnace ◽

Electric Arc ◽

Support Vector ◽

Support Vector Data Description ◽

Arc Furnace ◽

Vector Data ◽

Dynamic Selection ◽

Data Description

As increasing data-driven control strategies are applied in electric arc furnace systems, the problem of novelty detection has drawn more attentions than before. The presence of outliers should be the main obstacle in practical applications for these advanced control techniques. To this end, this paper proposes a dynamically selective support vector data description model to discover novelties in electric arc furnace. In this model, support vector data description plays the role of base detector. Artificial outliers are generated with two objectives, one is to assist the dynamic selection, and the other is to optimize two parameters of support vector data description. Then clustering technique is used to determine the validation set for each test point. Finally, a probabilistic method is used to compute the competence of base detectors. In contrast to other novelty ensembles that have parallel structures, our ensemble model has a dynamic selection mechanism that could facilitate the mining of the potential of base detectors. Three synthetic and three real-world datasets are used to validate the effectiveness of the proposed detection model. Experimental results have approved our method by comparing it with several competitors.

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A pruned support vector data description-based outlier detection method: Applied to robust process monitoring

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220905951 ◽

2020 ◽

Vol 42 (11) ◽

pp. 2113-2126 ◽

Cited By ~ 2

Author(s):

Ping Yuan ◽

Zhizhong Mao ◽

Biao Wang

Keyword(s):

Process Monitoring ◽

Support Vector ◽

Support Vector Data Description ◽

Data Sets ◽

Vector Data ◽

Training Set ◽

Data Set ◽

Data Description ◽

One Class Classifier ◽

Comparative Results

Support vector data description (SVDD) is a boundary-based one-class classifier that has been widely used for process monitoring during recent years. However, in some applications where databases are often contaminated by outliers, the performance of SVDD would become deteriorated, leading to low detection rate. To this end, this paper proposes a pruned SVDD model in order to improve its robustness. In contrast to other robust SVDD models that are developed from the algorithmic level, we prune the basic SVDD from a data level. The rationale is to exclude outlier examples from the final training set as many as possible. Specifically, three different SVDD models are constructed successively with different training sets. The first model is used to extract target points by means of rejecting more suspect outlier examples. The second model is constructed using those extracted target points, and is used to recover some false outlier examples labeled by the first model. We build the third (final) model with the final training set consisting of target examples by the first model and false outlier examples by the second model. We validate our proposed method on 20 benchmark data sets and TE data set. Comparative results show that our pruned model could improve the robustness of SVDD more efficiently.

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