ACM Computing Surveys (CSUR)
Network anomaly detection based on Eigen equation compression
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Methodologies for model-free data interpretation of civil engineering structures
Computers and Structures
Leadership discovery when data correlatively evolve
World Wide Web
Advanced Engineering Informatics
Anomaly localization for network data streams with graph joint sparse PCA
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2nd Conference on Wireless Health
Two effective methods to detect anomalies in embedded systems
Microelectronics Journal
Detecting leaders from correlated time series
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Detection of variable length anomalous subsequences in data streams
International Journal of Intelligent Information and Database Systems
On-line anomaly detection and resilience in classifier ensembles
Pattern Recognition Letters
Review: A review of novelty detection
Signal Processing
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This paper addresses the task of change analysis of correlated multi-sensor systems. The goal of change analysis is to compute the anomaly score of each sensor when we know that the system has some potential difference from a reference state. Examples include validating the proper performance of various car sensors in the automobile industry. We solve this problem based on a neighborhood preservation principle -If the system is working normally, the neighborhood graph of each sensor is almost invariant against the fluctuations of experimental conditions. Here a neighborhood graph is defined based on the correlation between sensor signals. With the notion of stochastic neighborhood, our method is capable of robustly computing the anomaly score of each sensor under conditions that are hard to be detected by other naive methods.