A Support Vector Data Description Committee for Face 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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An Integration Method Using Kernel Principal Component Analysis and Cascade Support Vector Data Description for Pipeline Leak Detection with Multiple Operating Modes

Processes ◽

10.3390/pr7100648 ◽

2019 ◽

Vol 7 (10) ◽

pp. 648 ◽

Cited By ~ 3

Author(s):

Mengfei Zhou ◽

Qiang Zhang ◽

Yunwen Liu ◽

Xiaofang Sun ◽

Yijun Cai ◽

...

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Leak Detection ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Operating Modes

Pipelines are one of the most efficient and economical methods of transporting fluids, such as oil, natural gas, and water. However, pipelines are often subject to leakage due to pipe corrosion, pipe aging, pipe weld defects, or damage by a third-party, resulting in huge economic losses and environmental degradation. Therefore, effective pipeline leak detection methods are important research issues to ensure pipeline integrity management and accident prevention. The conventional methods for pipeline leak detection generally need to extract the features of leak signal to establish a leak detection model. However, it is difficult to obtain actual leakage signal data samples in most applications. In addition, the operating modes of pipeline fluid transportation process often have frequent changes, such as regulating valves and pump operation. Aiming at these issues, this paper proposes a hybrid intelligent method that integrates kernel principal component analysis (KPCA) and cascade support vector data description (Cas-SVDD) for pipeline leak detection with multiple operating modes, using data samples that are leak-free during pipeline operation. Firstly, the local mean decomposition method is used to denoise and reconstruct the measured signal to obtain the feature variables. Then, the feature dimension is reduced and the nonlinear principal component is extracted by the KPCA algorithm. Secondly, the K-means clustering algorithm is used to identify multiple operating modes and then obtain multiple support vector data description models to obtain the decision boundaries of the corresponding hyperspheres. Finally, pipeline leak is detected based on the Cas-SVDD method. The experimental results show that the proposed method can effectively detect small leaks and improve leak detection accuracy.

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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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Fault detection in an engine by fusing information from multivibration sensors

International Journal of Distributed Sensor Networks ◽

10.1177/1550147717719057 ◽

2017 ◽

Vol 13 (7) ◽

pp. 155014771771905 ◽

Cited By ~ 5

Author(s):

Ruili Zeng ◽

Lingling Zhang ◽

Jianmin Mei ◽

Hong Shen ◽

Huimin Zhao

Keyword(s):

Fault Detection ◽

Evidence Theory ◽

Vibration Signal ◽

Support Vector ◽

Support Vector Data Description ◽

Detection Accuracy ◽

Vector Data ◽

Data Description ◽

Vibration Sensors ◽

Single Sensor

Fault detection based on the vibration signal of an engine is an effective non-disassembly method for engine diagnosis because a vibration signal includes a lot of information about the condition of the engine. To obtain multi-information for this article, three vibration sensors were placed at different test points to collect vibration information about the engine operating process. A method combining support vector data description and Dempster–Shafer evidence theory was developed for engine fault detection, where support vector data description is used to recognize the data from a single sensor and Dempster–Shafer evidence theory is used to classify the information from the three vibration sensors in detail. The experimental results show that the fault detection accuracy using three sensors is higher than using a single sensor. The multi-complementary sensor information can be adopted in the proposed method, which will increase the reliability of fault detection and reduce uncertainty in the recognition of a fault.

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

Lecture Notes in Electrical Engineering - Proceedings of 2017 Chinese Intelligent Automation Conference ◽

10.1007/978-981-10-6445-6_32 ◽

2017 ◽

pp. 283-293 ◽

Cited By ~ 1

Author(s):

Lili Yin ◽

Huangang Wang ◽

Wenhui Fan ◽

Qingkai Wang

Keyword(s):

Active Learning ◽

Novelty Detection ◽

Large Data ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Set ◽

Data Description ◽

Large Data Set

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MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

Current Bioinformatics ◽

10.2174/1574893615999200607173829 ◽

2020 ◽

Vol 15 ◽

Author(s):

Yi Zou ◽

Hongjie Wu ◽

Xiaoyi Guo ◽

Li Peng ◽

Yijie Ding ◽

...

Keyword(s):

Dna Binding ◽

Binding Proteins ◽

Detection Efficiency ◽

Dna Binding Proteins ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Multiple Kernel ◽

Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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