Multivariate Triangular Quantile Maps for Novelty Detection
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Date
2024-05-21
Authors
Wang, Jingjing
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
Novelty detection, a fundamental task in the field of machine learning, has drawn a lot of recent attention due to its wide-ranging applications and the rise of neural approaches. In this thesis, we present a general framework for neural novelty detection that centers around a multivariate extension of the univariate quantile function. Our general framework unifies and extends many classical and recent novelty detection algorithms, and opens the way to exploit recent advances in flow-based neural density estimation. We adapt the multiple gradient descent algorithm to obtain the first efficient end-to-end implementation of our framework that is free of tuning hyperparameters. Extensive experiments over a number of synthetic and real datasets confirm the efficacy of our proposed method against state-of-the-art alternatives.
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Keywords
novelty detection, multivariate quantile, normalizing flow