Novelty detection is the identification of new or unknown data that a machine learning system has not been trained with and was not previously aware of, with the help of either statistical or neural network based approaches.
Novelty detection is one of the fundamental requirements of a good classification system. A machine learning system can never be trained with all the possible object classes and hence the performance of the network will be poor for those classes that are under-represented in the training set. A good classification system must have the ability to differentiate between known and unknown objects during testing. For this purpose, different models for novelty detection have been proposed.
Novelty detection is a hard problem in machine learning since it depends on the statistics of the already known information. A generally applicable, parameter-free method for outlier detection in a high-dimensional space is not yet known. Novelty detection finds a variety of applications especially in signal processing, computer vision, pattern recognition, data mining and robotics. Another important application is the detection of a disease or potential fault whose class may be under-represented in the training set.
The statistical approaches to novelty detection may be classified into parametric and non-parametric approaches. Parametric approaches assume a specific statistical distribution (such as a Gaussian distribution) of data and statistical modeling based on data mean and covariance, whereas non-parametric approaches do not make any assumption on the statistical properties of data.
- M. Markou, S. Singh, Novelty detection: A review, part 1: Statistical approaches, Signal Processing 83, 2481–2497, 2003
- Novelty Detection in Learning Systems.Stephen Marsland, Division of Imaging Science and Biomedical Engineering, Stopford Building, The University of Manchester, Oxford Road, Manchester M13 9PL, UK.
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