Abstract
Deep outlier detection on high dimensional data is an important research problem with critical applications in many areas. Though promising performance has been demonstrated, we observe that existing methods characterized outliers only from a single perspective, which leads to reducing the distinction of inliers/outliers with the growth of training epochs. This in turn hurts the robustness and effectiveness of outlier detection since the optimal training epoch on a special dataset is unknown in unsupervised scenarios. In this paper, we propose a DNN based framework with both global and local structure discrimination for effective and robust Outlier Detection, named GOOD. The global module compacts the data (mainly inliers) since the majority of data are inliers, while the local module scatters the data (mainly outliers) based on that outliers reside in low-probability density areas. These two modules are cleverly united by a self-adaptive weighting strategy that trades off the degree of complementary and competitive cooperation. The complementary views can help effectively detect outliers with diverse characteristics, and such competitive learning can prevent a single module from learning the entire data too well and ensure robust detection performance. Comprehensive experimental studies on datasets from diverse domains show that GOOD significantly outperforms state-of-the-art methods by up to 30\(\%\) improvement of AUC while performing much more robustly with the growth of training epochs.
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
Notes
- 1.
KDDCUP99 contains semantically real inliers and outliers, so no further inlier sampling is adopted.
- 2.
Note that this is different from the default settings of training epochs in the corresponding papers since different datasets are adopted here, thus the reported results may also be different.
References
Ahmed, M., Mahmood, A.N., Islam, M.R.: A survey of anomaly detection techniques in financial domain. FGCS 55, 278–288 (2016)
An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Spec. Lect. IE 2(1) (2015)
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: ESANN (2013)
Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. TPAMI 33(8), 1548–1560 (2010)
Campos, G.O., et al.: On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. DMKD 30(4), 891–927 (2016)
Cao, L., Yang, D., Wang, Q., Yu, Y., Wang, J., Rundensteiner, E.A.: Scalable distance-based outlier detection over high-volume data streams. In: ICDE, pp. 76–87. IEEE (2014)
Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. arXiv preprint arXiv:1901.03407 (2019)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. CSUR 41(3), 15 (2009)
Chen, J., Sathe, S., Aggarwal, C., Turaga, D.: Outlier detection with autoencoder ensembles. In: SDM, pp. 90–98. SIAM (2017)
Cheng, L., Wang, Y., Liu, X., Li, B.: Outlier detection ensemble with embedded feature selection. In: AAAI, pp. 3503–3512 (2020)
Erfani, S.M., Rajasegarar, S., Karunasekera, S., Leckie, C.: High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning. Pattern Recogn. 58, 121–134 (2016)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: a new benchmark collection for text categorization research. JMLR 5, 361–397 (2004)
Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: ICDM, pp. 413–422. IEEE (2008)
Makhzani, A., Frey, B.: K-sparse autoencoders. arXiv preprint arXiv:1312.5663 (2013)
Masci, J., Meier, U., Cireşan, D., Schmidhuber, J.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) Artificial Neural Networks and Machine Learning. ICANN 2011, pp. 52–59. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21735-7_7
Pang, G., Ting, K.M., Albrecht, D.: Lesinn: detecting anomalies by identifying least similar nearest neighbours. In: ICDMW, pp. 623–630. IEEE (2015)
Ruff, L., et al.: Deep one-class classification. In: ICML, pp. 4393–4402 (2018)
Sabokrou, M., Fayyaz, M., Fathy, M., Klette, R.: Fully convolutional neural network for fast anomaly detection in crowded scenes. arXiv preprint arXiv:1609.00866 (2016)
Sakurada, M., Yairi, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: MLSDA Workshop, p. 4. ACM (2014)
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) Information Processing in Medical Imaging. IPMI 2017, pp. 146–157. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_12
Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)
Singh, A.: Anomaly detection for temporal data using long short-term memory (LSTM) (2017)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD cup 99 data set. In: CISDA, pp. 1–6. IEEE (2009)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: ICML, pp. 1096–1103. ACM (2008)
Wang, S., et al.: Effective end-to-end unsupervised outlier detection via inlier priority of discriminative network. In: NeurIPS, pp. 5960–5973 (2019)
Xia, Y., Cao, X., Wen, F., Hua, G., Sun, J.: Learning discriminative reconstructions for unsupervised outlier removal. In: ICCV, pp. 1511–1519 (2015)
Xu, D., Ricci, E., Yan, Y., Song, J., Sebe, N.: Learning Deep Representations of Appearance and Motion for Anomalous Event Detection, pp. 8.1–8.12 (2015)
Zenati, H., Romain, M., Foo, C.S., Lecouat, B., Chandrasekhar, V.: Adversarially learned anomaly detection. In: ICDM, pp. 727–736. IEEE (2018)
Zhang, J., Zulkernine, M.: Anomaly based network intrusion detection with unsupervised outlier detection. In: ICC, vol. 5, pp. 2388–2393. IEEE (2006)
Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: SIGKDD, pp. 665–674. ACM (2017)
Zong, B., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Huang, C., Cheng, L., Yao, F., He, R. (2024). Global and Local Structure Discrimination for Effective and Robust Outlier Detection. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14332. Springer, Singapore. https://doi.org/10.1007/978-981-97-2390-4_20
Download citation
DOI: https://doi.org/10.1007/978-981-97-2390-4_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2389-8
Online ISBN: 978-981-97-2390-4
eBook Packages: Computer ScienceComputer Science (R0)