Anomaly Detection and Removal Using Non-Stationary Gaussian Processes
By Steven Reece, Roman Garnett, Michael A. Osborne, Stephen J. Roberts. 2015
11 citations recorded by the source corpus.
This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. We assume that only one fault occurs at any one time and model the signal by two separate non-parametric Gaussian process models for both the physical phenomenon and the fault. In order to facilitate fault removal we introduce the Markov Region Link kernel for handling non-stationary Gaussian processes. This kernel is piece-wise stationary but guarantees that functions generated by it and their derivatives (when required) are everywhere continuous. We apply this kernel to the removal of drift and bias errors in faulty sensor data and also to the recovery of EOG artifact corrupted EEG signals.
Subfields: Artificial Intelligence, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Michael A. Osborne
- Stephen J. Roberts
- Suzanne Aigrain
- Steven Reece
- Nando de Freitas
- W. D. Penny
- Mark A. Girolami
- Ziyun Wang
In Control and Systems Engineering
Citations in the corpus
- Gaussian processes for time-series modelling (2013, methodology): “[11,12], in which covariances for biased readings, stuck sensors and sensor drifts are discussed.”