Anomaly Detection Based on Sparse Bayesian Regression

doi:10.3969/j.issn.1006-2475.2015.01.011

Abstract

Abstract: The data can be regarded as outliers highly intermixed with normal data in the field of anomaly detection. With minimal loss of normal information in the model, outliers are viewed as the top K samples holding maximal abnormal information in a dataset. Inspired by this idea, an anomaly detection model based on sparse bayesian regression which taken the Bayesian inferring framework into traditional kernel function was proposed to find the sample serious deviated from the model though the result of regress estimation. Experiment results show that this algorithm is of good sparsity and detection accuracy.

Key words: sparse Bayesian regression, residual method, anomaly detection, regress estimation, sparsity

CLC Number:

TP18

苏乐群，冯爱民. Anomaly Detection Based on Sparse Bayesian Regression[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.01.011.

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