The Living Thing / Notebooks : Anomaly detection

Working out when something you are seeing is not something you should have been expecting, in some sense which will not be made rigorous here, not yet.

Nuit Blanche has a roundup.

Special case: “trend detection”. e.g. Gnip Trend detection

Brauckhoff, D., Salamatian, K., & May, M. (2009) Applying PCA for Traffic Anomaly Detection: Problems and Solutions. In IEEE INFOCOM 2009 (pp. 2866–2870). DOI.
Girdhar, Y., Cho, W., Campbell, M., Pineda, J., Clarke, E., & Singh, H. (2015) Anomaly Detection in Unstructured Environments using Bayesian Nonparametric Scene Modeling. arXiv:1509.07979 [cs].
Henrickson, S., Kolb, J., Lehmann, B., & Montague, J. (2015) Trend detection in social data. . Twitter
Impagliazzo, R., & Wigderson, A. (1997) P = BPP if E Requires Exponential Circuits: Derandomizing the XOR Lemma. In Proceedings of the Twenty-ninth Annual ACM Symposium on Theory of Computing (pp. 220–229). New York, NY, USA: ACM DOI.
Kontostathis, A., Galitsky, L. M., Pottenger, W. M., Roy, S., & Phelps, D. J.(2004) A Survey of Emerging Trend Detection in Textual Data Mining. In M. W. Berry (Ed.), Survey of Text Mining (pp. 185–224). Springer New York
Kontostathis, A., Holzman, L. E., & Pottenger, W. M.(n.d.) Use of Term Clusters for Emerging Trend Detection.
Lindenbaum, O., Yeredor, A., & Salhov, M. (2015) Learning Coupled Embedding Using MultiView Diffusion Maps. In E. Vincent, A. Yeredor, Z. Koldovský, & P. Tichavský (Eds.), Latent Variable Analysis and Signal Separation (pp. 127–134). Springer International Publishing
Lopez, J. A., Camps, O., & Sznaier, M. (2015) Robust Anomaly Detection Using Semidefinite Programming. arXiv:1504.00905 [cs, Math].

Nikolov, S. (2012) Trend or no trend : a novel nonparametric method for classifying time series (Thesis). . Massachusetts Institute of Technology

This one looks fun, and comes with a cute explanation of how you might do this nonparametrically.
Peng, Z., Gurram, P., Kwon, H., & Yin, W. (2015) Optimal Sparse Kernel Learning for Hyperspectral Anomaly Detection. arXiv:1506.02585 [cs].
Rajasegarar, S., Leckie, C., Palaniswami, M., & Bezdek, J. C.(2006) Distributed Anomaly Detection in Wireless Sensor Networks. In 10th IEEE Singapore International Conference on Communication systems, 2006. ICCS 2006 (pp. 1–5). DOI.
Ringberg, H., Soule, A., Rexford, J., & Diot, C. (2007) Sensitivity of PCA for Traffic Anomaly Detection. In Proceedings of the 2007 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (pp. 109–120). New York, NY, USA: ACM DOI.
Soule, A., Salamatian, K., & Taft, N. (2005) Combining Filtering and Statistical Methods for Anomaly Detection. In Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement (pp. 31–31). Berkeley, CA, USA: USENIX Association
Wang, D., Wu, P., Zhao, P., & Hoi, S. C. H.(2015) A Framework of Sparse Online Learning and Its Applications. arXiv:1507.07146 [cs].
Wong, W.-K., Moore, A., Cooper, G., & Wagner, M. (2002) Rule-based Anomaly Pattern Detection for Detecting Disease Outbreaks. In Eighteenth National Conference on Artificial Intelligence (pp. 217–223). Menlo Park, CA, USA: American Association for Artificial Intelligence
Yi, Wang, Chen, G., & Maggioni, M. (2015) High Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies. arXiv:1509.07497 [stat].