Effective And Efficient Approach for Detecting Outliers

Abstract

Now a days in machine learning research anomaly detection is the main topic. Anomaly detection is the process of identifying unusual behavior. It is widely used in data mining, for example, medical informatics, computer vision, computer security, sensor networks. Statistical approach aims to find the outliers which deviate from such distributions. Most distribution models are assumed univariate, and thus the lack of robustness for multidimensional data. We proposed an online and conditional anomaly detection method based on oversample PCA osPCA with LOO strategy will amplify the effect of outliers. We can successfully use the variation of the dominant principal direction to identify the presence of rare but abnormal data, for conditional anomaly detection expectation-maximization algorithms for learning the model is used. Our approach is reducing computational costs and memory requirements.