@inproceedings{81556d5edb3140b4af3d1062700a7dbd,
title = "Robust mixture models for anomaly detection",
abstract = "We propose robust density estimation in a low dimensional space for anomaly detection. The outline of the method is as follows: first a low dimensional representation of the original data is learnt. Then, a robust density mixture model is estimated in the learnt space. Finally, the likelihood of a data point given the model parameters is used to apply anomaly detection. An efficient way for adapting the model parameters when the data distribution is changing with time is proposed. We further show how to identify the actual parameters in the original feature space that accounts for the occurrence of the anomaly. We present experimental results that demonstrate the effectiveness of the proposed methods.",
author = "Oren Barkan and Amir Averbuch",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings ; Conference date: 13-09-2016 Through 16-09-2016",
year = "2016",
month = nov,
day = "8",
doi = "10.1109/MLSP.2016.7738885",
language = "الإنجليزيّة",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Kostas Diamantaras and Aurelio Uncini and Palmieri, \{Francesco A. N.\} and Jan Larsen",
booktitle = "2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings",
address = "الولايات المتّحدة",
}