Abstract
We focus on the problem of identifying samples in a set that do not conform to structured patterns represented by low-dimensional manifolds. An effective way to solve this problem is to embed data in a high dimensional space, called Preference Space, where anomalies can be identified as the most isolated points. In this work, we employ Locality Sensitive Hashing to avoid explicit computation of distances in high dimensions and thus improve Anomaly Detection efficiency. Specifically, we present an isolation-based anomaly detection technique designed to work in the Preference Space which achieves state-of-the-art performance at a lower computational cost. Code is publicly available at https://github.com/ineveLoppiliF/Hashing-for-Structure-based-Anomaly-Detection.
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References
Ahmed, M., Mahmood, A.N., Islam, M.R.: A survey of anomaly detection techniques in financial domain. Future Gener. Comput. Syst. 55, 278–288 (2016)
Miljković, D.: Fault detection methods: a literature survey. In: International Convention on Information. Communication and Electronic Technology, pp. 750–755. IEEE (2011)
Lazarevic, A., Ertoz, L., Kumar, V., Ozgur, A., Srivastava, J.: A comparative study of anomaly detection schemes in network intrusion detection. In: International Conference on Data Mining, pp. 25–36. SIAM (2003)
Ukil, A., Bandyoapdhyay, S., Puri, C., Pal, A.: IoT healthcare analytics: the importance of anomaly detection. In: Advanced Information Networking and Applications, pp. 994–997. IEEE (2016)
De Benedetti, M., Leonardi, F., Messina, F., Santoro, C., Vasilakos, A.: Anomaly detection and predictive maintenance for photovoltaic systems. Neurocomputing 310, 59–68 (2018)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1–58 (2009)
Ronen Basri and David W Jacobs. Lambertian reflectance and linear subspaces. Trans. Pattern Anal. Mach. Intell. 25(2), 218–233 (2003)
Leveni, F., Magri, L., Boracchi, G., Alippi, C.: PIF: anomaly detection via preference embedding. In: International Conference on Pattern Recognition, pp. 8077–8084. IEEE (2021)
Kruskal, J.B.: Nonmetric multidimensional scaling: a numerical method. Psychometrika 29(2), 115–129 (1964)
Ružička, M.: Anwendung mathematisch-statisticher methoden in der geobotanik (synthetische bearbeitung von aufnahmen). Biológia 13, 647 (1958)
Gionis, A., Indyk, P., Motwani, R., et al.: Similarity search in high dimensions via hashing. Conf. Very Large Databases 99, 518–529 (1999)
Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation-based anomaly detection. Trans. Knowl. DiscData 6(1), 1–39 (2012)
Hariri, S., Kind, M.C., Brunner, R.J.: Extended isolation forest. Trans. Knowl. Data Eng. 33(4), 1479–1489 (2019)
Lesouple, J., Baudoin, C., Spigai, M., Tourneret, J.-Y.: Generalized isolation forest for anomaly detection. Pattern Recogn. Lett. 149, 109–119 (2021)
Staerman, G., Mozharovskyi, P., Clémençon, S., Florence d’Alché, B.: Functional isolation forest. In: Asian Conference on Machine Learning, pp. 332–347. PMLR (2019)
Zhang, X., et al.: Lshiforest: a generic framework for fast tree isolation based ensemble anomaly analysis. In: International Conference on Data Engineering, pp. 983–994. IEEE (2017)
Magri, L., Fusiello, A.: Reconstruction of interior walls from point cloud data with min-hashed j-linkage. In International Conference on 3D Vision, pp. 131–139. IEEE (2018)
Lipkus, A.H.: A proof of the triangle inequality for the tanimoto distance. J. Math. Chem. 26(1–3), 263–265 (1999)
Toldo, R., Fusiello, A.: Robust multiple structures estimation with J-Linkage. In: European Conference on Computer Vision, pp. 537–547 (2008)
Magri, L., Fusiello, A.: T-Linkage: a continuous relaxation of J-linkage for multi-model fitting. In: Computer Vision and Pattern Recognition Conference, pp. 3954–3961, June 2014
Magri, L., Leveni, F., Boracchi, G.: Multilink: multi-class structure recovery via agglomerative clustering and model selection. In: Computer Vision and Pattern Recognition Conference, pp. 1853–1862 (2021)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Mensi, A., Bicego, M.: Enhanced anomaly scores for isolation forests. Pattern Recogn. 120, 108115 (2021)
Knuth, D.E.: The Art of Computer Programming: Sorting and Searching, vol. 3. Addison-Wesley Professional, Boston (1998)
Broder, A.Z., Charikar, M., Frieze, A.M., Mitzenmacher, M.: Min-wise independent permutations. In: Symposium on Theory of Computing, pp. 327–336 (1998)
Wong, H.S., Chin, T.J., Yu, J., Suter, D.: Dynamic and hierarchical multi-structure geometric model fitting. In: International Conference on Computer Vision, pp. 1044–1051. IEEE (2011)
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Leveni, F., Magri, L., Alippi, C., Boracchi, G. (2023). Hashing for Structure-Based Anomaly Detection. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14234. Springer, Cham. https://doi.org/10.1007/978-3-031-43153-1_3
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