Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Kernel PCA for novelty detection
Pattern Recognition
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
The novelty detection approach for different degrees of class imbalance
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Automatic bearing fault diagnosis based on one-class ν-SVM
Computers and Industrial Engineering
Review: A review of novelty detection
Signal Processing
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Novelty detection is a pattern recognition technique used when there is one well characterized normal state and the abnormal (or novel) states are poorly described because of lack of data. Data deficiency of these states may arise due to cost and difficulty in measuring them - e.g. failed equipment states in equipment health monitoring. Normal pattern recognition techniques have a wide array of methods for reducing the number of features initially employed to characterize classes. These techniques are of limited use in novelty detection primarily because they are focused on representing the data accurately in a subspace rather than on finding a subspace where the classes can easily be discriminated, or they are optimized to distinguish between all classes rather than on focusing on distinguishing solely between normal and abnormal classes. The proposed methodology will enable feature extraction in unbalanced classification problems where the well-sampled normal data is expected to be orbited by the under-sampled fault data. The technique will be demonstrated to work well with artificial and actual machinery health data.