TY - GEN
T1 - Group anomaly detection using deep generative models
AU - Chalapathy, Raghavendra
AU - Toth, Edward
AU - Chawla, Sanjay
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points. In particular, we perform group anomaly detection (GAD) with an emphasis on irregular group distributions (e.g. irregular mixtures of image pixels). GAD is an important task in detecting unusual and anomalous phenomena in real-world applications such as high energy particle physics, social media and medical imaging. In this paper, we take a generative approach by proposing deep generative models: Adversarial autoencoder (AAE) and variational autoencoder (VAE) for group anomaly detection. Both AAE and VAE detect group anomalies using point-wise input data where group memberships are known a priori. We conduct extensive experiments to evaluate our models on real world datasets. The empirical results demonstrate that our approach is effective and robust in detecting group anomalies. Code related to this paper is available at: https://github.com/raghavchalapathy/gad, https://www.cs.cmu.edu/~lxiong/gad/gad.html, https://github.com/jorjasso/SMDD-group-anomaly-detection, https://github.com/cjlin1/libsvm.
AB - Unlike conventional anomaly detection research that focuses on point anomalies, our goal is to detect anomalous collections of individual data points. In particular, we perform group anomaly detection (GAD) with an emphasis on irregular group distributions (e.g. irregular mixtures of image pixels). GAD is an important task in detecting unusual and anomalous phenomena in real-world applications such as high energy particle physics, social media and medical imaging. In this paper, we take a generative approach by proposing deep generative models: Adversarial autoencoder (AAE) and variational autoencoder (VAE) for group anomaly detection. Both AAE and VAE detect group anomalies using point-wise input data where group memberships are known a priori. We conduct extensive experiments to evaluate our models on real world datasets. The empirical results demonstrate that our approach is effective and robust in detecting group anomalies. Code related to this paper is available at: https://github.com/raghavchalapathy/gad, https://www.cs.cmu.edu/~lxiong/gad/gad.html, https://github.com/jorjasso/SMDD-group-anomaly-detection, https://github.com/cjlin1/libsvm.
KW - Adversarial
KW - Auto-encoders
KW - Group anomaly detection
KW - Variational
UR - http://www.scopus.com/inward/record.url?scp=85061156104&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-10925-7_11
DO - 10.1007/978-3-030-10925-7_11
M3 - Conference contribution
AN - SCOPUS:85061156104
SN - 9783030109240
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 173
EP - 189
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings
A2 - Bonchi, Francesco
A2 - Gärtner, Thomas
A2 - Hurley, Neil
A2 - Ifrim, Georgiana
A2 - Berlingerio, Michele
PB - Springer Verlag
T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018
Y2 - 10 September 2018 through 14 September 2018
ER -