Anomaly detection for categorical observations using latent gaussian process

Fengmao Lv, Guowu Yang, Jinzhao Wu, Chuan Liu, Yuhong Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Anomaly detection is an important problem in many applications, ranging from medical informatics to network security. Various distribution-based techniques have been proposed to tackle this issue, which try to learn the probabilistic distribution of conventional behaviors and consider the observations with low densities as anomalies. For categorical observations, multinomial or dirichlet compound multinomial distributions were adopted as effective statistical models for conventional samples. However, when faced with small-scale data set containing multivariate categorical samples, these models will suffer from the curse of dimensionality and fail to capture the statistical properties of conventional behavior, since only a small proportion of possible categorical configurations will exist in the training data. As an effective bayesian non-parametric technique, categorical latent Gaussian process is able to model small-scale categorical data through learning a continuous latent space for multivariate categorical samples with Gaussian process. Therefore, on the basis of categorical latent Gaussian process, we propose an anomaly detection technique for multivariate categorical observations. In our method, categorical latent Gaussian process is adopted to capture the probabilistic distributions of conventional categorical samples. Experimental results on categorical data set show that our method can effectively detect anomalous categorical observations and achieve better detection performance compared with other anomaly detection techniques.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
EditorsDongbin Zhao, Yuanqing Li, El-Sayed M. El-Alfy, Derong Liu, Shengli Xie
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319701387
StatePublished - 2017
Event24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
Duration: Nov 14 2017Nov 18 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10638 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other24th International Conference on Neural Information Processing, ICONIP 2017

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.


  • Anomaly detection
  • Bayesian non-parametric model
  • Categorical data
  • Data-efficient learning
  • Gaussian process


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