Online Budgeted Stochastic Coordinate Ascent for Large-Scale Kernelized Dual Support Vector Machine Training

2020 ◽  
pp. 23-47
Author(s):  
Sahar Qaadan ◽  
Abhijeet Pendyala ◽  
Merlin Schüler ◽  
Tobias Glasmachers
Keyword(s):  
Support Vector Machine ◽  
Large Scale ◽  
Support Vector ◽  
Support Vector Machine Training

scholarly journals Fast outlier detection for high-dimensional data of wireless sensor networks

2020 ◽  
Vol 16 (10) ◽  
pp. 155014772096383
Author(s):  
Yan Qiao ◽  
Xinhong Cui ◽  
Peng Jin ◽  
Wu Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Outlier Detection ◽  
Large Scale ◽  
Deep Belief Network ◽  
Wireless Sensor ◽  
High Dimensional ◽  
Support Vector ◽  
Belief Network

This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called “deep belief network online quarter-sphere support vector machine,” which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%–5%.

scholarly journals Weighted Linear Loss Support Vector Machine for Large Scale Problems

2014 ◽  
Vol 31 ◽  
pp. 639-647 ◽  
Author(s):  
Yuan-Hai Shao ◽  
Zhen Wang ◽  
Zhi-Min Yang ◽  
Nai-Yang Deng
Keyword(s):  
Support Vector Machine ◽  
Large Scale ◽  
Support Vector ◽  
Large Scale Problems ◽  
Linear Loss
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