원문정보
초록
영어
Special Complex non-Gaussian processes may have dynamic operation scenario shifts so that the traditional Outlier detection approaches become ill-suited. This paper proposes a new outlier detection approach based on using subspace learning and Gaussian mixture model(GMM) in energy disaggregation. Locality preserving projections(LPP) of subspace learning can optimally preserve the neighborhood structure, reveal the intrinsic manifold structure of the data and keep outliers far away from the normal sample compared with the principal component analysis (PCA). The results show proposed approach can significantly improve performance of outlier detection in energy disaggregation, increase the fraction true-positive from 93.8% to 97%, decrease the fraction false-positive from 35.48% to 25.8%.
목차
1. Introduction
2. LPP of Subspace Learning
3. Outlier Detection Using GMM Based On LPP
4. Experiment and Result Analysis
4.1. Data Description of Energy Disaggregation In House
4.2. Experiments and Result Analysis
5. Conclusion and Future Work
Acknowledgements
References