This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
A. Adler, M. Elad, Y. Hel-Or, and E. Rivlin, “Sparse coding with anomaly detection”, Journal of Signal Processing Systems, vol. 79, no. 2, 2015, pp. 179–188, doi:10.1007/s11265-014-0913-0.A.AdlerM.EladY.Hel-OrE.Rivlin “Sparse coding with anomaly detection”, Journal of Signal Processing Systems, vol. 79, no. 2, 2015, pp. 179–188, 10.1007/s11265-014-0913-0.Open DOISearch in Google Scholar
C. B. Barber, D. P. Dobkin, and H. Huhdanpaa, “The quickhull algorithm for convex hulls”, ACM Transactions on Mathematical Software (TOMS), vol. 22, no. 4, 1996, pp. 469–483, doi:10.1145/235815.235821.C. B.BarberD. P.DobkinH.Huhdanpaa “The quickhull algorithm for convex hulls”, ACM Transactions on Mathematical Software (TOMS), vol. 22, no. 4, 1996, pp. 469–483, 10.1145/235815.235821.Open DOISearch in Google Scholar
A. Boukerche, L. Zheng, and O. Alfandi, “Outlier detection”, ACM Computing Surveys, vol. 53, no. 3, 2021, pp. 1–37, doi:10.1145/3381028.A.BoukercheL.ZhengO.Alfandi “Outlier detection”, ACM Computing Surveys, vol. 53, no. 3, 2021, pp. 1–37, 10.1145/3381028.Open DOISearch in Google Scholar
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers”, vol. 3, no. 1, 2010, pp. 1–122, doi:10.1561/2200000016.S.BoydN.ParikhE.ChuB.PeleatoJ.Eck-stein “Distributed optimization and statistical learning via the alternating direction method of multipliers”, vol. 3, no. 1, 2010, pp. 1–122, 10.1561/2200000016.Open DOISearch in Google Scholar
S. P. Boyd and L. Vandenberghe, Convex optimization, Cambridge University Press, 2004.S. P.BoydL.VandenbergheConvex optimization, Cambridge University Press, 2004.Search in Google Scholar
P. Casale, O. Pujol, and P. Radeva, “Approximate convex hulls family for one-class classification”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6713 LNCS, 2011, pp. 106–115, doi:10.1007/978-3- 642-21557-5_13.P.CasaleO.PujolP.Radeva “Approximate convex hulls family for one-class classification”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6713 LNCS, 2011, pp. 106–115, 10.1007/978-3- 642-21557-5_13.Open DOISearch in Google Scholar
V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection”, ACM Computing Surveys, vol. 41, no. 3, 2009, pp. 1–58, doi:10.1145/1541880.1541882.V.ChandolaA.BanerjeeV.Kumar “Anomaly detection”, ACM Computing Surveys, vol. 41, no. 3, 2009, pp. 1–58, 10.1145/1541880.1541882.Open DOISearch in Google Scholar
W.-C. Chang, C.-P. Lee, ·. Chih, and J. Lin. “A revisit to support vector data description”. Technical report, Department of Computer Science at National Taiwan University, Taipei, Taiwan, 2013.W.-C.ChangC.-P.Lee·.ChihJ.Lin. “A revisit to support vector data description”. Technical report, Department of Computer Science at National Taiwan University, Taipei, Taiwan, 2013.Search in Google Scholar
R. Domingues, M. Filippone, P. Michiardi, and J. Zouaoui, “A comparative evaluation of outlier detection algorithms: Experiments and analyses”, Pattern Recognition, vol. 74, 2018, pp. 406– 421, doi:10.1016/J.PATCOG.2017.09.037.R.DominguesM.FilipponeP.MichiardiJ.Zouaoui “A comparative evaluation of outlier detection algorithms: Experiments and analy-ses”, Pattern Recognition, vol. 74, 2018, pp. 406–421, 10.1016/J.PATCOG.2017.09.037.Open DOISearch in Google Scholar
J. Eckstein and W. Yao. “Understanding the convergence of the alternating direction method of multipliers: Theoretical and computational perspectives”. Technical report, 2015.J.EcksteinW.Yao “Understanding the convergence of the alternating direction method of multipliers: Theoretical and computational per-spectives”. Technical report, 2015.Search in Google Scholar
M. Fukushima, “Application of the alternating direction method of multipliers to separable convex programming problems”, Computational Optimization and Applications, vol. 1, no. 1, 1992, pp. 93–111, doi:10.1007/BF00247655.M.Fukushima “Application of the alternating direction method of multipliers to separable convex programming problems”, Computational Optimization and Applications, vol. 1, no. 1, 1992, pp. 93–111, 10.1007/BF00247655.Open DOISearch in Google Scholar
W. Hilal, S. A. Gadsden, and J. Yawney, “Financial fraud”, Expert Systems with Applications, vol. 193, 2022, doi:10.1016/J.ESWA.2021.116429.W.HilalS. A.GadsdenJ.Yawney “Financial fraud”, Expert Systems with Applications, vol. 193, 2022, 10.1016/J.ESWA.2021.116429.Open DOISearch in Google Scholar
J. Huang, A. J. Smola, A. Gretton, K. M. Borgwardt, and B. Schölkopf, “Correcting sample selection bias by unlabeled data”, NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems, 2006, pp. 601– 608, doi:10.7551/mitpress/7503.003.0080.J.HuangA. J.SmolaA.GrettonK. M.BorgwardtB.Schölkopf “Correcting sample selection bias by unlabeled data”, NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems, 2006, pp. 601–608, 10.7551/mitpress/7503.003.0080.Open DOISearch in Google Scholar
I. Kalliantzis, A. N. Papadopoulos, A. Gounaris, and K. Tsichlas. “Efficient distributed outlier detection in data streams”. Technical report, 2019.I.KalliantzisA. N.PapadopoulosA.GounarisK.Tsichlas “Efficient distributed outlier detection in data streams”. Technical report, 2019.Search in Google Scholar
T. Kanamori, S. Hido, and M. Sugiyama, “Efficient direct density ratio estimation for non-stationarity adaptation and outlier detection”, Advances in Neural Information Processing Systems 21-Proceedings of the 2008 Conference, 2009, pp. 809–816.T.KanamoriS.HidoM.Sugiyama “Efficient direct density ratio estimation for non-stationarity adaptation and outlier detection”, Advances in Neural Information Processing Systems 21-Proceedings of the 2008 Conference, 2009, pp. 809–816.Search in Google Scholar
J. D. Kelleher, B. Mac Namee, and D’Arcy Aoife, Fundamentals of machine learning for predictive data analytics, MIT Press, 2020.J. D.KelleherB.Mac NameeD’ArcyAoifeFundamentals of machine learning for predictive data analytics, MIT Press, 2020.Search in Google Scholar
C.-N. Li, Y.-H. Shao, W. Yin, and M.-Z. Liu, “Robust and sparse linear discriminant analysis via an alternating direction method of multipliers”, IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 3, 2020, pp. 915– 926, doi:10.1109/TNNLS.2019.2910991.C.-N.LiY.-H.ShaoW.YinM.-Z.Liu “Robust and sparse linear discriminant analysis via an alternating direction method of multipliers”, IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 3, 2020, pp. 915–926, 10.1109/TNNLS.2019.2910991.Open DOISearch in Google Scholar
M. M. Moya and D. R. Hush, “Network constraints and multi-objective optimization for one-class classification”, Neural Networks, vol. 9, no. 3, 1996, pp. 463–474, doi:10.1016/0893- 6080(95)00120-4.M. M.MoyaD. R.Hush “Network constraints and multi-objective optimization for one-class classification”, Neural Networks, vol. 9, no. 3, 1996, pp. 463–474, 10.1016/0893-6080(95)00120-4.Open DOISearch in Google Scholar
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: machine learning in Python”, Journal of Machine Learning Research, vol. 12, no. 85, 2011, pp. 2825–2830.F.PedregosaG.VaroquauxA.GramfortV.MichelB.ThirionO.GriselM.BlondelP.PrettenhoferR.WeissV.DubourgJ.VanderplasA.PassosD.CournapeauM.BrucherM.PerrotÉ.Duchesnay “Scikit-learn: machine learning in Python”, Journal of Machine Learning Research, vol. 12, no. 85, 2011, pp. 2825–2830.Search in Google Scholar
N. R. Prasad, S. Almanza-Garcia, and T. T. Lu, “Anomaly detection”, Computers, Materials and Continua, vol. 14, no. 1, 2009, pp. 1–22, doi:10.3970/cmc.2009.014.001.N. R.PrasadS.Almanza-GarciaT. T.Lu “Anomaly detection”, Computers, Materials and Continua, vol. 14, no. 1, 2009, pp. 1–22, 10.3970/cmc.2009.014.001.Open DOISearch in Google Scholar
N. N. R. Ranga Suri, N. Murty M, and G. Athithan, Outlier detection: Techniques and applications, Intelligent Systems Reference Library, Springer International Publishing, 2019, doi:10.1007/978-3-030-05127-3.N. N. R.Ranga SuriN.Murty MG.AthithanOutlier detection: Techniques and applications, Intelligent Systems Reference Library, Springer International Publishing, 2019, 10.1007/978-3-030-05127-3.Open DOISearch in Google Scholar
G. Ranganathan, “Real time anomaly detection techniques using PySpark frame Work”, Journal of Artificial Intelligence and Capsule Networks, vol. 2, no. 1, 2020, pp. 20–30, doi:10.36548/jaicn.2020.1.003.G.Ranganathan “Real time anomaly detection techniques using PySpark frame Work”, Journal of Artificial Intelligence and Capsule Networks, vol. 2, no. 1, 2020, pp. 20–30, 10.36548/jaicn.2020.1.003.Open DOISearch in Google Scholar
B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, “Estimating the support of a high-dimensional distribution”, Neural Computation, vol. 13, no. 7, 2001, pp. 1443–1471, doi:10.1162/089976601750264965.B.SchölkopfJ. C.PlattJ.Shawe-TaylorA. J.SmolaR. C.Williamson “Estimating the support of a high-dimensional distribution”, Neural Computation, vol. 13, no. 7, 2001, pp. 1443–1471, 10.1162/089976601750264965.Open DOISearch in Google Scholar
B. Schölkopf, R. C. Williamson, A. Smola, and J. Shawe-Taylor, “SV estimation ofa distribution’s support”. In: Neural Information Processing Systems (NIPS), 2000, pp. 582–588.B.SchölkopfR. C.WilliamsonA.SmolaJ.Shawe-Taylor “SV estimation ofa distribution’s support”. In: Neural Information Processing Systems (NIPS), 2000, pp. 582–588.Search in Google Scholar
D. M. Tax and R. P. Duin, “Support vector data description”, Machine Learning, vol. 54, no. 1, 2004, pp. 45–66, doi:10.1023/B:MACH.0000008084.60811.49.D. M.TaxR. P.Duin “Support vector data description”, Machine Learning, vol. 54, no. 1, 2004, pp. 45–66, 10.1023/B:MACH.0000008084.60811.49.Open DOISearch in Google Scholar
T. Wang, M. Cai, X. Ouyang, Z. Cao, T. Cai, X. Tan, and X. Lu, “Anomaly detection based on convex analysis: A survey”, Frontiers in Physics, vol. 10, 2022, pp. 873–848, doi:10.3389/FPHY.2022.873848/BIBTEX.T.WangM.CaiX.OuyangZ.CaoT.CaiX.TanX.Lu “Anomaly detection based on convex analysis: A survey”, Frontiers in Physics, vol. 10, 2022, pp. 873–848, 10.3389/FPHY.2022.873848/BIBTEX.Open DOISearch in Google Scholar
K. Zhang, I. W. Tsang, and J. T. Kwok, “Improved Nyström low-rank approximation and error analysis”. In: Proceedings of the 25th international conference on Machine learning-ICML ‘08, New York, New York, USA, 2008, pp. 1232–1239, doi:10.1145/1390156.1390311.K.ZhangI. W.TsangJ. T.Kwok “Improved Nyström low-rank approximation and error analysis”. In: Proceedings of the 25th international conference on Machine learning-ICML ‘08, New York, New York, USA, 2008, pp. 1232–1239, 10.1145/1390156.1390311.Open DOISearch in Google Scholar
S. Zhang, V. Ursekar, and L. Akoglu, “Sparx: Distributed outlier detection at scale”. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2022, pp. 4530–4540, doi:10.1145/3534678.3539076.S.ZhangV.UrsekarL.Akoglu “Sparx: Distributed outlier detection at scale”. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2022, pp. 4530–4540, 10.1145/3534678.3539076.Open DOISearch in Google Scholar
Y. Zhao, PyOD documentacion release 1.0.9, USC, 2023.Y.ZhaoPyOD documentacion release 1.0.9, USC, 2023.Search in Google Scholar
Y. Zhao, Z. Nasrullah, and Z. Li. “PyOD: A Python toolbox for scalable outlier detection”. Technical report, 2019.Y.ZhaoZ.NasrullahZ.Li “PyOD: A Python toolbox for scalable outlier detection”. Technical report, 2019.Search in Google Scholar