Outlier Detection for Transformer's Oil Chromatographic Data Based on Metric Learning and the Weighted Local Outlier Factor
Detecting the outliers for transformer's oil chromatographic data is very important for analyzing and monitoring the status of a transformer. However, the existing methods are usually developed for some specific situations and may not perform well in many cases. In this paper, we proposed an outlier detection method based on metric learning and weighted local outlier factor (WLOF) We firstly considered the data as points in the high dimensional space and learning a Mahalanobis matrix according to a training set. And then we used WLOF to assign the degree of being an outlier for every point. Metric learning can make a regularization and reduce the correlation between different components of a vector. And WLOF method can detect the outliers accurately even there are many duplicate data points. Results on the real data set showed that the proposed method performs well in outlier detection even the different components of a vector are not similar in scale and have strong correlations.
2019 6th International Conference on Systems and Informatics (ICSAI)