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Boundary sample extraction support vector data description: a novel anomaly detection method for large-scale data
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AI
DOI：10.1088/1361-6501/ada6ea.png)
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Abstract
En 中文
Support vector data description (SVDD) has been effectively used in many anomaly detection problems. Equipped with kernel functions, its training complexity grows exponentially with the increase in training data, which makes it less practical for large-scale datasets. In this paper, we propose a boundary sample extraction SVDD (BSE-SVDD) anomaly detection method based on data reduction, aiming for use with large-scale data. Firstly, the BSE mechanism is established on the basis of demonstrating that the spatial position of the sample is related to its corresponding Lagrange multiplier. Then, the BSE mechanism is used to search for the local optimal solution of the Lagrange multiplier, and all the training samples are sorted. Finally, the top p of ranked samples are extracted as boundary samples for training, while most of the training samples that may be non-support vectors are removed. Compared with SVDD studies based on data reduction, the experimental results on large-scale datasets show that BSE-SVDD can obtain comparable classification accuracy with greatly improved training speed.
Keywords:
support vector data description
boundary samples extraction
data reduction
stochastic gradient descent
large-scale data
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