A Support Vector Data Description Approach to NLOS Identification in UWB Positioning

Non-line-of-sight (NLOS) propagation is one of the most important challenges in radio positioning, and, in recent years, significant attention has been drawn to the identification and mitigation of NLOS signals. This paper focuses on the identification of NLOS signals. The authors consider the NLOS identification problem as a one-class classification problem and apply the support vector data description (SVDD), providing accurate data descriptions utilizing kernel techniques, to perform NLOS identification in ultrawide bandwidth (UWB) positioning. Our work is based on the fact that some features extracted from the received signal waveforms, such as the kurtosis, the mean excess delay spread, and the root mean square delay spread, are different between line-of-sight (LOS) and NLOS signals. Numerical simulations are performed to demonstrate the performance, using a dataset derived from a measurement campaign.

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Monitoring the mean with least-squares support vector data description

Gestão & Produção ◽

10.1590/1806-9649-2021v28e019 ◽

2021 ◽

Vol 28 (3) ◽

Author(s):

Edgard M. Maboudou-Tchao

Keyword(s):

Least Squares ◽

Statistical Process Control ◽

Control Charts ◽

Classification Problem ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Statistical Process ◽

Data Description ◽

The Mean

Abstract: Multivariate control charts are essential tools in multivariate statistical process control (MSPC). “Shewhart-type” charts are control charts using rational subgroupings which are effective in the detection of large shifts. Recently, the one-class classification problem has attracted a lot of interest. Three methods are typically used to solve this type of classification problem. These methods include the k−center method, the nearest neighbor method, one-class support vector machine (OCSVM), and the support vector data description (SVDD). In industrial applications, like statistical process control (SPC), practitioners successfully used SVDD to detect anomalies or outliers in the process. In this paper, we reformulate the standard support vector data description and derive a least squares version of the method. This least-squares support vector data description (LS-SVDD) is used to design a control chart for monitoring the mean vector of processes. We compare the performance of the LS-SVDD chart with the SVDD and T2 chart using out-of-control Average Run Length (ARL) as the performance metric. The experimental results indicate that the proposed control chart has very good performance.

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The Research on Semi-Supervised Support Vector Data Description Multi-Classification Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.1115 ◽

2011 ◽

Vol 268-270 ◽

pp. 1115-1120

Author(s):

De Qian Xue

Keyword(s):

Learning Difficulties ◽

Classification Problem ◽

Classification Algorithm ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Degree Of Membership ◽

Simulation Results ◽

Multi Classification

Semi-supervised Support Vector Data Description multi-classification algorithm is presented, in order to solve less labeled data learning, difficulties in the implementation and poor results of semi-supervised multi-classification, which full use the distribution of information in of non-target samples. S3VDD-MC algorithm defines the degree of membership of non-target samples, in order to get the non-target samples’ accepted labels or refused labels, on this basis, several super-spheres constructed, a k-classification problem is transformed into k SVDDs problem. Finally, the simulation results verify the effectiveness of the algorithm.

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KERNEL WHITENING FOR ONE-CLASS CLASSIFICATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s021800140300240x ◽

2003 ◽

Vol 17 (03) ◽

pp. 333-347 ◽

Cited By ~ 37

Author(s):

DAVID M. J. TAX ◽

PIOTR JUSZCZAK

Keyword(s):

Feature Space ◽

Support Vector ◽

Support Vector Data Description ◽

Good Representation ◽

Vector Data ◽

Data Description ◽

Unit Variance ◽

Good Distinction ◽

One Class Classification ◽

Target Data

In one-class classification one tries to describe a class of target data and to distinguish it from all other possible outlier objects. Obvious applications are areas where outliers are very diverse or very difficult or expensive to measure, such as in machine diagnostics or in medical applications. In order to have a good distinction between the target objects and the outliers, good representation of the data is essential. The performance of many one-class classifiers critically depends on the scaling of the data and is often harmed by data distributions in (nonlinear) subspaces. This paper presents a simple preprocessing method which actively tries to map the data to a spherical symmetric cluster and is almost insensitive to data distributed in subspaces. It uses techniques from Kernel PCA to rescale the data in a kernel feature space to unit variance. This transformed data can now be described very well by the Support Vector Data Description, which basically fits a hypersphere around the data. The paper presents the methods and some preliminary experimental results.

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One Class Classification for Anomaly Detection: Support Vector Data Description Revisited

Advances in Data Mining. Applications and Theoretical Aspects - Lecture Notes in Computer Science ◽

10.1007/978-3-642-23184-1_3 ◽

2011 ◽

pp. 25-39 ◽

Cited By ~ 14

Author(s):

Eric J. Pauwels ◽

Onkar Ambekar

Keyword(s):

Anomaly Detection ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

One Class Classification

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Compressor Surge Detection Based on Support Vector Data Description

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.152-154.1545 ◽

2012 ◽

Vol 152-154 ◽

pp. 1545-1549

Author(s):

Chang Zheng Li ◽

Yong Lei

Keyword(s):

Support Vector Machine ◽

Active Control ◽

Axial Flow ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Compressor Surge ◽

Aerodynamic Instability ◽

One Class Classification

Axial flow compressors work as an indispensable device in industry fields. Surge is a phenomenon of aerodynamic instability, which characterized by disruption of flow. When a compressor works in surge state, the vibration is so intense that it may causes accidents. Detecting surge timely and accurately not only insure safety of compressors but also is a key of active control of aerodynamic instability. Support vector data description (SVDD) is a one-class classification method developed based on the theory of support vector machine (SVM). In this paper, we introduce SVDD into the field of compressor surge detection. It demonstrates that SVDD method can give a warning far ahead of surge.

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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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