Detect the unexpected: Novelty detection in large astrophysical surveys using fisher vectors

Michael Rotman, Itamar Reis, Dovi Poznanski, Lior Wolf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Finding novelties in an untagged high dimensional dataset poses an open question. In this work, we present an innovative method for detecting such novelties using Fisher Vectors. Our dataset distribution is modeled using a Gaussian Mixture Model. An anomaly score that stems from the theory of Fisher Vector is computed for each of the samples. We compute the anomaly score on the SDSS galaxies spectra dataset and present the different types of novelties found. We compare our findings with other outlier detection algorithms from the literature, and demonstrate the ability of our method to distinguish between samples taken from intersecting probability distributions.

Original languageEnglish
Title of host publicationIC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
EditorsAna Fred, Joaquim Filipe
PublisherSciTePress
Pages124-134
Number of pages11
ISBN (Electronic)9789897583827
DOIs
StatePublished - 2019
Event11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019 - Vienna, Austria
Duration: 17 Sep 201919 Sep 2019

Publication series

NameIC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Volume1

Conference

Conference11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019
Country/TerritoryAustria
CityVienna
Period17/09/1919/09/19

Keywords

  • Anomaly Detection
  • Fisher Vectors
  • Galaxies Spectra

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