Multivariate Triangular Quantile Maps for Novelty Detection
| dc.contributor.author | Wang, Jingjing | |
| dc.date.accessioned | 2024-05-21T15:53:13Z | |
| dc.date.available | 2024-05-21T15:53:13Z | |
| dc.date.issued | 2024-05-21 | |
| dc.date.submitted | 2024-05-16 | |
| dc.description.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. | en |
| dc.identifier.uri | http://hdl.handle.net/10012/20578 | |
| dc.language.iso | en | en |
| dc.pending | false | |
| dc.publisher | University of Waterloo | en |
| dc.subject | novelty detection | en |
| dc.subject | multivariate quantile | en |
| dc.subject | normalizing flow | en |
| dc.title | Multivariate Triangular Quantile Maps for Novelty Detection | en |
| dc.type | Master Thesis | en |
| uws-etd.degree | Master of Mathematics | en |
| uws-etd.degree.department | David R. Cheriton School of Computer Science | en |
| uws-etd.degree.discipline | Computer Science | en |
| uws-etd.degree.grantor | University of Waterloo | en |
| uws-etd.embargo.terms | 0 | en |
| uws.contributor.advisor | Yu, Yaoliang | |
| uws.contributor.affiliation1 | Faculty of Mathematics | en |
| uws.peerReviewStatus | Unreviewed | en |
| uws.published.city | Waterloo | en |
| uws.published.country | Canada | en |
| uws.published.province | Ontario | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |