Outlier_Detection_Research

classical methods

• LOF

  • Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. Lof: identifying densitybased local outliers. In ACM sigmod record, volume 29, pp. 93–104. ACM, 2000.

• OC-SVM

  • Bernhard Schölkopf, John C Platt, John Shawe-Taylor, Alex J Smola, and Robert C Williamson. Estimating the support of a high-dimensional distribution. Neural computation, 13(7):1443–1471, 2001.

• uses Benford’s Law to identify anomalies in social networks

  • Samuel Maurus and Claudia Plant. Let’s see your digits: Anomalous-state detection using benford’s law. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 977–986. ACM, 2017.

• uses Extreme Value Theory to detect anomalies

  • Alban Siffer, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouet. Anomaly detection in streams with extreme value theory. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1067–1075. ACM, 2017

specific type of data

• spatially contextualized data

  • Guanjie Zheng, Susan L Brantley, Thomas Lauvaux, and Zhenhui Li. Contextual spatial outlier detection with metric learning. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2161–2170. ACM, 2017.

• graph data

  • Bryan Perozzi, Leman Akoglu, Patricia Iglesias Sánchez, and Emmanuel Müller. Focused clustering and outlier detection in large attributed graphs. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1346–1355. ACM, 2014.
  • Bryan Perozzi and Leman Akoglu. Scalable anomaly ranking of attributed neighborhoods. In Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 207–215. SIAM, 2016.
  • Jundong Li, Harsh Dani, Xia Hu, and Huan Liu. Radar: Residual analysis for anomaly detection in attributed networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2152–2158. AAAI Press, 2017.
  • Ninghao Liu, Xiao Huang, and Xia Hu. Accelerated local anomaly detection via resolving attributed networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2337–2343. AAAI Press, 2017.
  • NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks Wenchao Yu, Wei Cheng, Charu C. Aggarwal, Kai Zhang, Haifeng Chen, Wei Wang. KDD,2018

specific structure

• energy

  • （DSEBM）Shuangfei Zhai, Yu Cheng, Weining Lu, and Zhongfei Zhang. Deep structured energy based models for anomaly detection. arXiv preprint arXiv:1605.07717, 2016.

• GAN

  • （AnoGAN）Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein, Ursula Schmidt-Erfurth, and Georg Langs.Unsupervised anomaly detection with generative adversarial networks to guide marker discovery.In International Conference on Information Processing in Medical Imaging, pp. 146–157. Springer,2017.

• autoencoders

  • （DAE）Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In International Conference on Machine learning, pp. 1096–1103. ACM, 2008.

  • Chong Zhou and Randy C Paffenroth. Anomaly detection with robust deep autoencoders. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 665–674. ACM, 2017.

  • （DAGMM）Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In International Conference on Learning Representations, 2018.

• DCN

  • Bo Yang, Xiao Fu, Nicholas D Sidiropoulos, and Mingyi Hong. Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In International Conference on Machine Learning, pp. 3861–3870, 2017.

Active Anomaly Detection

• Nico Görnitz, Marius Kloft, Konrad Rieck, and Ulf Brefeld. Toward supervised anomaly detection. Journal of Artificial Intelligence Research, 46:235–262, 2013.

• （LODA-AAD）Shubhomoy Das, Weng-Keen Wong, Thomas Dietterich, Alan Fern, and Andrew Emmott. Incorporating expert feedback into active anomaly discovery. In International Conference on Data Mining (ICDM), pp. 853–858. IEEE, 2016.

• （TREE-AAD）Shubhomoy Das, Weng-Keen Wong, Alan Fern, Thomas G Dietterich, and Md Amran Siddiqui. Incorporating feedback into tree-based anomaly detection. Workshop on Interactive Data Exploration and Analytics (IDEA), 2017.

• UaiNets: From Unsupervised to Active Deep Anomaly Detection .Tiago Pimentel, Marianne Monteiro, Juliano Viana, Adriano Veloso, Nivio Ziviani,ICLR,2018

To be continue

• Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and F Huang. A tutorial on energy-based learning. Predicting structured data, 1(0), 2006.

• Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, and Tom Soderstrom. 2018. Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18).ACM, New York, NY, USA, 387–395.

• Daehyung Park, Yuuna Hoshi, and Charles C. Kemp. 2018. A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder. IEEE Robotics and Automation Letters 3 (2018), 1544–1551

• Marco Fraccaro, Søren Kaae Sønderby, Ulrich Paquet, and Ole Winther. 2016.Sequential neural models with stochastic layers. In Advances in neural information processing systems. 2199–2207.

• Daehyung Park, Yuuna Hoshi, and Charles C. Kemp. 2018. A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder. IEEE Robotics and Automation Letters 3 (2018), 1544–1551.

• Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In International Conference on Learning Representations.

• Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes. In Proceedings of the Second International Conference on Learning Representations(ICLR 2014).

• Christian Robert and George Casella. 2013. Monte Carlo statistical methods. Springer Science & Business Media

• Genshiro Kitagawa and Will Gersch. 1996. Linear Gaussian State Space Modeling. In Smoothness Priors Analysis of Time Series. Springer, 55–65

• Rui Zhao, Wanli Ouyang, Hongsheng Li, and Xiaogang Wang. 2015. Saliency detection by multi-context deep learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1265–1274.(saliency detection research/saliency detection research when sufficient labeled data is available)

• Alban Siffer, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouet.Anomaly detection in streams with extreme value theory. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1067–1075.

• （SR） Xiaodi Hou and Liqing Zhang. 2007. Saliency detection: A spectral residual approach. In Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on. IEEE, 1–8.

• Chenlei Guo, Qi Ma, and Liming Zhang. 2008. Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. (2008)

VAE

• （DONUT）Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li,Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, et al. 2018. Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications.In Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 187–196.(proposed DONUT, an unsupervised anomaly detection method based on Variational Auto-Encoder (VAE)、evaluation strategy)
• （OmniAnomaly）Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun and Dan Pei.Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network. KDD 2019
• M. S¨olch, J. Bayer, M. Ludersdorfer, and P. van der Smagt. Variational inference for on-line anomaly detection in high-dimensional time series. arXiv preprint arXiv:1602.07109, 2016.
• H. Xu, W. Chen, N. Zhao, Z. Li, J. Bu, Z. Li, Y. Liu,Y. Zhao, D. Pei, Y. Feng, et al. Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications. arXiv preprint arXiv:1802.03903, 2018.
• S. Clachar. Novelty detection and cluster analysis in time series data using variational autoencoder feature maps. The University of North Dakota, 2016.

trajectory（from video sequence）

• （层级聚类）Fu Z, Hu W, Tan T. Similarity based vehicle trajectory clustering and anomaly detection[C]//IEEE International Conference on Image Processing 2005. IEEE, 2005, 2: II-602
• （SVM）Piciarelli C, Micheloni C, Foresti G L. Trajectory-based anomalous event detection[J]. IEEE Transactions on Circuits and Systems for video Technology, 2008, 18(11): 1544-1554.
• （k-means）Zhou Y, Yan S, Huang T S. Detecting anomaly in videos from trajectory similarity analysis[C]//2007 IEEE International Conference on Multimedia and Expo. IEEE, 2007: 1087-1090.

trajectory

• Kong X, Song X, Xia F, et al. LoTAD: Long-term traffic anomaly detection based on crowdsourced bus trajectory data[J]. World Wide Web, 2018, 21(3): 825-847.

evaluation metric

• Alexander Lavin and Subutai Ahmad. 2015. Evaluating Real-Time Anomaly Detection Algorithms–The Numenta Anomaly Benchmark. In Machine Learning and Applications (ICMLA), 2015 IEEE 14th International Conference on. IEEE, 38–44

轨迹的dataset

https://www.neard.ws/archives/823