Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

@inproceedings{Zong2018DeepAG,
  title={Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection},
  author={Bo Zong and Qi Song and Martin Renqiang Min and Wei Cheng and Cristian Lumezanu and Daeki Cho and Haifeng Chen},
  year={2018}
}
Unsupervised anomaly detection on multior high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruitful progress, they mainly suffer from decoupled model learning with inconsistent optimization goals and incapability of preserving essential information in the low-dimensional space… CONTINUE READING
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