Detect the Unexpected: Novelty Detection in Large Astrophysical Surveys using Fisher Vectors

Michael Rotman, Itamar Reis, Dovi Poznanski, Lior Wolf

2019

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.

Paper Citation

in Harvard Style

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) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 124-134. DOI: 10.5220/0008163301240134

in Bibtex Style

@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) - Volume 1: KDIR},
year={2019},
pages={124-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008163301240134},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Detect the Unexpected: Novelty Detection in Large Astrophysical Surveys using Fisher Vectors
SN - 978-989-758-382-7
AU - Rotman M.
AU - Reis I.
AU - Poznanski D.
AU - Wolf L.
PY - 2019
SP - 124
EP - 134
DO - 10.5220/0008163301240134
PB - SciTePress