Abstract
In many research and application fields, anomaly detection always is an important issue. In the article, a method of anomaly detection is presented which based on Lasso on the basis of variable linear regression solution of the Lasso problem. We transform the process of anomaly detection into a linear regression model, meanwhile, take the detection parameter as regression variables and establish the model of the regression variables and the dependent variable. Due to estimation of Lasso parameter own stable regression coefficient, can compress parameters of the model and reduce the number of parameters. Those characteristics accord with the requirement of stability, high-speed and simplicity which are needful for anomaly detection. Experimental results show that our method has higher detection accuracy and more rapid convergence ability under the constraints of the appropriate threshold.
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Acknowledgements
This study was supported by the National Natural Science Foundation of China (61303227), Chinese Postdoctoral Science Foundation (2015M580765), ChongQing Postdoctoral Science Foundation (Xm2016041), the Fundamental Research Funds for the Central Universities (XDJK2014C039, XDJK2016C045), Doctoral Fund of Southwestern University (swu1114033), the project of Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJ1403106).
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Chen, S., Peng, M., Xiong, H. et al. An anomaly detection method based on Lasso. Cluster Comput 22 (Suppl 3), 5407–5419 (2019). https://doi.org/10.1007/s10586-017-1255-z
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DOI: https://doi.org/10.1007/s10586-017-1255-z