Abstract
Mining anomalous objects from multi-view data is a challenging issue as data collected from diverse sources have more complicated distributions and exhibit inconsistently heterogeneous properties. Existing multi-view outlier detection approaches mainly focus on transduction, which becomes very costly when new data points are introduced by an input stream. Besides, the existing detection methods use either the pairwise multiplication of cross-view data vectors to quantify outlier scores or the predicted joint probability to measure anomalousness, which are less extensible to support more sources. To resolve these challenges, we propose in this paper a Bayesian probabilistic model for finding multi-view outliers in an inductive learning manner. Specifically, we first follow the probabilistic projection method of latent variable for exploring the structural correlation and consistency of different views. Then, we seek for a promising Bayesian treatment for the generative model to approach the issue of selecting the optimal dimensionality. Next, we explore a variational approximation approach to estimate model parameters and achieve model selection. The outlier score for every sample can then be predicted by analyzing mutual distances of its representations across different views in the latent subspace. Finally, we benchmark the efficacy of the proposed method by conducting comprehensive experiments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622–637. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20893-6_39
Archambeau, C., et al.: Sparse probabilistic projections. In: NIPS, pp. 73–80 (2009)
Chalapathy, R., et al.: Deep learning for anomaly detection: a survey. CoRR (2019)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1–58 (2009)
Chaudhuri, K., Kakade, S.M., Livescu, K., Sridharan, K.: Multi-view clustering via canonical correlation analysis. In: ICML, pp. 129–136 (2009)
Chen, M., Weinberger, K.Q., Chen, Y.: Automatic feature decomposition for single view co-training. In: ICML (2011)
Christoudias, M., et al.: Bayesian localized multiple kernel learning (2009)
Diethe, T., Hardoon, D.R., Shawe-Taylor, J.: Multiview fisher discriminant analysis. In: NeurIPS Workshop on Learning from Multiple Sources (2008)
Du, B., Zhang, L.: A discriminative metric learning based anomaly detection method. TGRS 52(11), 6844–6857 (2014)
Fyfe, C., Lai, P.L.: ICA using kernel canonical correlation analysis. In: Proceedings of International Workshop on Independent Component Analysis and Blind Signal Separation (2000)
Gao, J., et al.: A spectral framework for detecting inconsistency across multi-source object relationships. In: ICDM, pp. 1050–1055. IEEE (2011)
Hotelling, H.: Relations between two sets of variates. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics. Springer Series in Statistics (Perspectives in Statistics), pp. 162–190. Springer, Heidelberg (1992). https://doi.org/10.1007/978-1-4612-4380-9_14
Iwata, T., Yamada, M.: Multi-view anomaly detection via robust probabilistic latent variable models. In: NeurIPS, pp. 1136–1144 (2016)
Izakian, H., Pedrycz, W.: Anomaly detection in time series data using a fuzzy c-means clustering. In: IFSA/NAFIPS, pp. 1513–1518. IEEE (2013)
Ji, Y.X., et al.: Multi-view outlier detection in deep intact space. In: ICDM, pp. 1132–1137. IEEE (2019)
Li, K., Li, S., Ding, Z., Zhang, W., Fu, Y.: Latent discriminant subspace representations for multi-view outlier detection. In: AAAI (2018)
Li, S., Shao, M., Fu, Y.: Multi-view low-rank analysis for outlier detection. In: SDM, pp. 748–756. SIAM (2015)
Liu, A.Y., Lam, D.N.: Using consensus clustering for multi-view anomaly detection. In: SPW, pp. 117–124. IEEE (2012)
Liu, B., Xiao, Y., Philip, S.Y., Hao, Z., Cao, L.: An efficient approach for outlier detection with imperfect data labels. TKDE 26(7), 1602–1616 (2013)
Liu, F.T., et al.: Isolation-based anomaly detection. TKDD 6(1), 1–39 (2012)
Marcos Alvarez, A., Yamada, M., Kimura, A., Iwata, T.: Clustering-based anomaly detection in multi-view data. In: CIKM, pp. 1545–1548. ACM (2013)
Na, G.S., Kim, D., Yu, H.: DILOF: Effective and memory efficient local outlier detection in data streams. In: KDD, pp. 1993–2002 (2018)
Pang, G., Cao, L., Liu, H.: Learning representations of ultrahigh-dimensional data for random distance-based outlier detection. In: KDD, pp. 2041–2050 (2018)
Pei, Y., Zaiane, O.R., Gao, Y.: An efficient reference-based approach to outlier detection in large datasets. In: ICDM, pp. 478–487. IEEE (2006)
Quadrianto, N., Lampert, C.H.: Learning multi-view neighborhood preserving projections. In: ICML (2011)
Senator, T.E., et al.: Detecting insider threats in a real corporate database of computer usage activity. In: KDD, pp. 1393–1401 (2013)
Sheng, X.R., Zhan, D.C., Lu, S., Jiang, Y.: Multi-view anomaly detection: neighborhood in locality matters. In: AAAI, vol. 33, pp. 4894–4901 (2019)
Shon, A., Grochow, K., Hertzmann, A., Rao, R.P.: Learning shared latent structure for image synthesis and robotic imitation. In: NeurIPS, pp. 1233–1240 (2006)
Spirin, N., Han, J.: Survey on web spam detection: principles and algorithms. ACM SIGKDD Explor. Newsl. 13(2), 50–64 (2012)
Suo, X., et al.: Sparse canonical correlation analysis. CoRR (2017)
Tong, H., Lin, C.Y.: Non-negative residual matrix factorization with application to graph anomaly detection. In: SDM, pp. 143–153. SIAM (2011)
Wang, Z., et al.: Inductive multi-view semi-supervised anomaly detection via probabilistic modeling. In: ICBK, pp. 257–264. IEEE (2019)
Wang, Z., Lan, C.: Towards a hierarchical Bayesian model of multi-view anomaly detection. In: IJCAI (2020)
Wu, S., Wang, S.: Information-theoretic outlier detection for large-scale categorical data. TKDE 25(3), 589–602 (2011)
Xia, T., et al.: Multiview spectral embedding. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 40(6), 1438–1446 (2010)
Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. CoRR (2013)
Zhao, H., Fu, Y.: Dual-regularized multi-view outlier detection. In: IJCAI (2015)
Zimek, A., Gaudet, M., Campello, R.J., Sander, J.: Subsampling for efficient and effective unsupervised outlier detection ensembles. In: KDD, pp. 428–436 (2013)
Acknowledgement
The authors would like to thank the support from Zhejiang Lab (111007-PI2001) and Zhejiang Provincial Natural Science Foundation (LZ21F030001).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Z. et al. (2021). Learning Probabilistic Latent Structure for Outlier Detection from Multi-view Data. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-75762-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75761-8
Online ISBN: 978-3-030-75762-5
eBook Packages: Computer ScienceComputer Science (R0)