- Aggarwal, C.C. (2016). Outlier Analysis Second Edition.
- Angiulli, F., & Pizzuti, C. (2002). Fast outlier detection in high dimensional spaces. In: European Conference on Principles of Data Mining and Knowledge Discovery. pp. 15–27. Springer.
- Arning, A., Agrawal, R., & Raghavan, P. (1996). A linear method for deviation detection in large databases. In: KDD. vol. 1141, pp. 972–981.
- Birgé, L., & Rozenholc, Y. (2006). How many bins should be put in a regular histogram. ESAIM: Probability and Statistics 10, 24–45.
- Breunig, M.M., Kriegel, H.P., Ng, R.T., & Sander, J. (2000). LOF: Identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data. pp. 93–104.
- Burgess, C.P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., Lerchner, A. (2018). Understanding disentangling in β − vae. arXiv preprint arXiv:1804.03599.
- Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR) 41(3), 1–58.
- Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory 13(1), 21–27.
- Goldstein, M., & Dengel, A. (2012). Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. KI-2012: poster and demo track 9.
- Hanley, J.A., & McNeil, B.J. (1982). The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143(1), 29–36.
- He, Z., Xu, X., & Deng, S. (2003). Discovering cluster-based local outliers. Pattern recognition letters 24(9-10), 1641–1650.
- Janssens, J., Huszár, F., Postma, E., & van den Herik, H. (2012). Stochastic outlier selection. Tilburg Centre for Creative Computing, techreport 1.
- Kingma, D.P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
- Kriegel, H.P., Kröger, P., Schubert, E., & Zimek, A. (2009). Outlier detection in axis-parallel subspaces of high dimensional data. In: Pacific-Asia conference on knowledge discovery and data mining. pp. 831–838. Springer.
- Latecki, L.J., Lazarevic, A., & Pokrajac, D. (2007). Outlier detection with kernel density functions. In: International Workshop on Machine Learning and Data Mining in Pattern Recognition. pp. 61–75. Springer.
- Lazarevic, A., & Kumar, V. (2005). Feature bagging for outlier detection. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. pp. 157–166.
- Lei, S., Zheng, R., Zhang, S., Wang, S., Chen, R., Sun, K., Zeng, H., Zhou, J., & Wei, W. (2021). Global patterns of breast cancer incidence and mortality: A population-based cancer registry data analysis from 2000 to 2020. Cancer Communications 41(11), 1183–1194.
- Li, Z., Zhao, Y., Botta, N., Ionescu, C., & Hu, X. (2020). COPOD: Copula-based outlier detection. In: 2020 IEEE International Conference on Data Mining (ICDM). pp. 1118–1123. IEEE.
- Li, Z., Zhao, Y., Hu, X., Botta, N., Ionescu, C., & Chen, G. (2022). ECOD: Unsupervised outlier detection using empirical cumulative distribution functions. IEEE Transactions on Knowledge and Data Engineering.
- Liu, F.T., Ting, K.M., & Zhou, Z.H. (2008). Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining. pp. 413–422. IEEE.
- Liu, F.T., Ting, K.M., & Zhou, Z.H. (2012). Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data (TKDD) 6(1), 1–39.
- Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M., & He, X. (2019). Generative adversarial active learning for unsupervised outlier detection. IEEE Transactions on Knowledge and Data Engineering 32(8), 1517–1528.
- Lotter, W., Diab, A.R., Haslam, B., Kim, J.G., Grisot, G., Wu, E., Wu, K., Onieva, J.O., Boyer, Y., Boxerman, J.L., et al. (2021). Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nature Medicine 27(2), 244–249.
- Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision 60(2), 91–110.
- Patro, S., & Sahu, K.K. (2015). Normalization: A preprocessing stage. arXiv preprint arXiv:1503.06462.
- Pevný, T. (2016). LODA: Lightweight on-line detector of anomalies. Machine Learning 102(2), 275–304.
- Ramaswamy, S., Rastogi, R., & Shim, K. (2000). Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data. pp. 427–438.
- Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to sift or surf. In: 2011 International conference on computer vision. pp. 2564–2571. IEEE.
- Ruff, L., Vandermeulen, R., Goernitz, N., Deecke, L., Siddiqui, S.A., Binder, A., Müller, E., & Kloft, M. (2018). Deep one-class classification. In: International conference on machine learning. pp. 4393–4402. PMLR.
- Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., & Langs, G. (2017). Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: International conference on information processing in medical imaging. pp. 146–157. Springer.
- Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., & Williamson, R.C. (2001). Estimating the support of a high-dimensional distribution. Neural computation 13(7), 1443–1471.
- Shvetsova, N., Bakker, B., Fedulova, I., Schulz, H., & Dylov, D.V. (2021). Anomaly detection in medical imaging with deep perceptual autoencoders. IEEE Access 9, 118571–118583.
- Shyu, M.L., Chen, S.C., Sarinnapakorn, K., & Chang, L. (2003). A novel anomaly detection scheme based on principal component classifier. Tech. rep., Miami Univ Coral Gables Fl Dept of Electrical and Computer Engineering.
- Smiti, A. (2020). A critical overview of outlier detection methods. Computer Science Review 38, 100306.
- Sugiyama, M., & Borgwardt, K. (2013). Rapid distance-based outlier detection via sampling. Advances in neural information processing systems 26.
- Tang, J., Chen, Z., Fu, A.W.C., & Cheung, D.W. (2002). Enhancing effectiveness of outlier detections for low density patterns. In: Pacific-Asia conference on knowledge discovery and data mining. pp. 535–548. Springer.
- Yusuf, A., Dima, R., & Aina, S. (2021). Optimized breast cancer classification using feature selection and outliers detection. Journal of the Nigerian Society of Physical Sciences pp. 298–307.
- Zhao, Y., Hu, X., Cheng, C., Wang, C., Wan, C., Wang, W., Yang, J., Bai, H., Li, Z., Xiao, C., et al. (2021). SUOD: Accelerating large-scale unsupervised heterogeneous outlier detection. Proceedings of Machine Learning and Systems 3, 463–478.
- Zhao, Y., Nasrullah, Z., & Li, Z. (2019). PyOD: A python toolbox for scalable outlier detection. Journal of Machine Learning Research 20(96), 1–7.