Journal
SIGNAL PROCESSING
Volume 83, Issue 12, Pages 2481-2497Publisher
ELSEVIER
DOI: 10.1016/j.sigpro.2003.07.018
Keywords
novelty detection review; statistical approaches; Gaussian mixture models; hidden Markov models; KNN; Parzen density estimation; string matching; clustering
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Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model. In this paper we provide state-of-the-art review in the area of novelty detection based on statistical approaches. The second part paper details novelty detection using neural networks. As discussed, there are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics. (C) 2003 Elsevier B.V. All rights reserved.
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