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Novelty Detection in Sequential Data by Informed Clustering and Modeling by Adilova, Linara; Chen, Siming; Kamp, Michael is a scholarly article available to read on EtoBox.

What is Novelty Detection in Sequential Data by Informed Clustering and Modeling about?

Novelty detection in discrete sequences is a challenging task, since deviations from the process generating the normal data are often small or intentionally hidden. Novelties can be detected by modeling normal sequences and measuring the deviations of a new sequence from the model predictions. However, in many applications data is generated by several distinct processes so that models trained on all the data tend to over-generalize and novelties remain undetected. We propose to approach this challenge through decomposition: by clustering the data we break down the problem, obtaining simpler modeling task in each cluster which can be modeled more accurately. However, this comes at a trade-off, since the amount of training data per cluster is reduced. This is a particular problem for discrete sequences where state-of-the-art models are data-hungry. The success of this approach thus depends on the quality of the clustering, i.e., whether the individual learning problems are sufficiently simpler than the joint problem. While clustering discrete sequences automatically is a challenging and domain-specific task, it is often easy for human domain experts, given the right tools. In this pa

Author

Adilova, Linara; Chen, Siming; Kamp, Michael

Published

2021

Language

EN