PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Known Unknowns: Novelty Detection in Condition Monitoring
John Quinn and Christopher Williams
In: 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), 6-8 Jun 2007, Girona, Spain.


In time-series analysis it is often assumed that observed data can be modelled as being derived from a number of regimes of dynamics, as e.g. in a Switching Kalman Filter (SKF) \cite{west-harrison-97,ghahramani-hinton-98}. However, it may not be possible to model all of the regimes, and in this case it can be useful to represent explicitly a `novel' regime. We apply this idea to the Factorial Switching Kalman Filter (FSKF) by introducing an extra factor (the `X-factor') to account for the unmodelled variation. We apply our method to physiological monitoring data from premature infants receiving intensive care, and demonstrate that the model is effective in detecting abnormal sequences of observations that are not modelled by the known regimes.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3365
Deposited By:Christopher Williams
Deposited On:14 February 2008