TY - GEN
T1 - Sparse Gaussian markov random field mixtures for anomaly detection
AU - Idé, Tsuyoshi
AU - Khandelwal, Ankush
AU - Kalagnanam, Jayant
PY - 2017/1/31
Y1 - 2017/1/31
N2 - We propose a new approach to anomaly detection from multivariate noisy sensor data. We address two major challenges: To provide variable-wise diagnostic information and to automatically handle multiple operational modes. Our task is a practical extension of traditional outlier detection, which is to compute a single scalar for each sample. To consistently define the variable-wise anomaly score, we leverage a predictive conditional distribution. We then introduce a mixture of Gaussian Markov random field and its Bayesian inference, resulting in a sparse mixture of sparse graphical models. Our anomaly detection method is capable of automatically handling multiple operational modes while removing unwanted nuisance variables.We demonstrate the utility of our approach using real equipment data from the oil industry.
AB - We propose a new approach to anomaly detection from multivariate noisy sensor data. We address two major challenges: To provide variable-wise diagnostic information and to automatically handle multiple operational modes. Our task is a practical extension of traditional outlier detection, which is to compute a single scalar for each sample. To consistently define the variable-wise anomaly score, we leverage a predictive conditional distribution. We then introduce a mixture of Gaussian Markov random field and its Bayesian inference, resulting in a sparse mixture of sparse graphical models. Our anomaly detection method is capable of automatically handling multiple operational modes while removing unwanted nuisance variables.We demonstrate the utility of our approach using real equipment data from the oil industry.
UR - http://www.scopus.com/inward/record.url?scp=85014547052&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85014547052&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2016.168
DO - 10.1109/ICDM.2016.168
M3 - Conference contribution
AN - SCOPUS:85014547052
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 955
EP - 960
BT - Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
A2 - Bonchi, Francesco
A2 - Wu, Xindong
A2 - Baeza-Yates, Ricardo
A2 - Domingo-Ferrer, Josep
A2 - Zhou, Zhi-Hua
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Data Mining, ICDM 2016
Y2 - 12 December 2016 through 15 December 2016
ER -