@inproceedings{1010af675ad64cfa895f7573fdd51427,
title = "Detect the unexpected: Novelty detection in large astrophysical surveys using fisher vectors",
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.",
keywords = "Anomaly Detection, Fisher Vectors, Galaxies Spectra",
author = "Michael Rotman and Itamar Reis and Dovi Poznanski and Lior Wolf",
note = "Publisher Copyright: Copyright {\textcopyright} 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved; 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2019 ; Conference date: 17-09-2019 Through 19-09-2019",
year = "2019",
doi = "10.5220/0008163301240134",
language = "אנגלית",
series = "IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
publisher = "SciTePress",
pages = "124--134",
editor = "Ana Fred and Joaquim Filipe",
booktitle = "IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management",
}