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Authors: Michael Rotman 1 ; Itamar Reis 2 ; Dovi Poznanski 2 and Lior Wolf 3

Affiliations: 1 School of Computer Science, Tel-Aviv University and Israel ; 2 School of Physics and Astronomy, Tel-Aviv University and Israel ; 3 School of Computer Science, Tel-Aviv University, Israel, Facebook AI Research and Israel

Keyword(s): Anomaly Detection, Galaxies Spectra, Fisher Vectors.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Rotman, M.; Reis, I.; Poznanski, D. and Wolf, L. (2019). Detect the Unexpected: Novelty Detection in Large Astrophysical Surveys using Fisher Vectors. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR; ISBN 978-989-758-382-7; ISSN 2184-3228, SciTePress, pages 124-134. DOI: 10.5220/0008163301240134

@conference{kdir19,
author={Michael Rotman. and Itamar Reis. and Dovi Poznanski. and Lior Wolf.},
title={Detect the Unexpected: Novelty Detection in Large Astrophysical Surveys using Fisher Vectors},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR},
year={2019},
pages={124-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008163301240134},
isbn={978-989-758-382-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR
TI - Detect the Unexpected: Novelty Detection in Large Astrophysical Surveys using Fisher Vectors
SN - 978-989-758-382-7
IS - 2184-3228
AU - Rotman, M.
AU - Reis, I.
AU - Poznanski, D.
AU - Wolf, L.
PY - 2019
SP - 124
EP - 134
DO - 10.5220/0008163301240134
PB - SciTePress