Stochastic Neighbourhood Components Analysis

References

• Bar-Hillel A, Hertz T, Shental N, Weinshall D (2003) Learning distance functions using equivalence relations. Fawcett T, Mishra N, eds. Proc. 20th Internat. Conf. Machine Learn. (AAAI Press, Washington, DC), 11–18.Google Scholar
• Bellet A, Habrard A, Sebban M (2013) A survey on metric learning for feature vectors and structured data. Preprint, submitted June 28, https://arxiv.org/abs/1306.6709.Google Scholar
• Cao L, Tay FE (2001) Financial forecasting using support vector machines. Neural Comput. Appl. 10(2):184–192.Google Scholar
• Davis JV, Kulis B, Jain P, Sra S, Dhillon IS (2007) Information-theoretic metric learning. Ghahramani Z, ed. Proc. 24th Internat. Conf. Machine Learn. (Association for Computing Machinery, New York), 209–216.Google Scholar
• Devroye LP, Wagner T (1980) Distribution-free consistency results in nonparametric discrimination and regression function estimation. Ann. Statist. 8(2):231–239.Google Scholar
• dos Santos CH, Montevechi JAB, de Queiroz JA, de Carvalho Miranda R, Leal F (2022) Decision support in productive processes through des and abs in the digital twin era: A systematic literature review. Internat. J. Production Res. 60(8):2662–2681.Google Scholar
• Dutta UK, Harandi M, Sekhar CC (2020) Unsupervised deep metric learning via orthogonality based probabilistic loss. IEEE Trans. Artificial Intelligence 1(1):74–84.Google Scholar
• Globerson A, Roweis S (2005) Metric learning by collapsing classes. Weiss Y, Schölkopf B, Platt JC, eds. Advances in Neural Information Processing Systems, vol. 18 (MIT Press, Cambridge, MA), 451–458.Google Scholar
• Goldberger J, Roweis ST, Hinton GE, Salakhutdinov RR (2005) Neighbourhood components analysis. Saul L, Weiss Y, Bottou L, eds. Advances in Neural Information Processing Systems, vol. 17 (MIT Press, Cambridge, MA), 513–520.Google Scholar
• Hong LJ, Jiang G (2019) Offline simulation online application: A new framework of simulation-based decision making. Asia-Pacific J. Oper. Res. 36(6):1–22.Google Scholar
• Horvat T, Job J (2020) The use of machine learning in sport outcome prediction: A review. WIREs Data Mining Knowledge Discovery 10(5):e1380.Google Scholar
• Jiang G, Hong LJ, Nelson BL (2020) Online risk monitoring using offline simulation. INFORMS J. Comput. 32(2):356–375.Abstract, Google Scholar
• Kayton D, Teyner T, Schwartz C, Uzsoy R (1996) Effects of dispatching and down time on the performance of wafer fabs operating under theory of constraints. Proc. 19th IEEE/CPMT Internat. Electronics Manufacturing Tech. Sympos. (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 49–56.Google Scholar
• Kedem D, Tyree S, Sha F, Lanckriet GR, Weinberger KQ (2012) Non-linear metric learning. Pereira F, Burges CJ, Bottou L, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 25 (Curran Associates, Red Hook, NY), 2573–2581.Google Scholar
• Keslin G, Nelson BL, Pagnoncelli BK, Plumlee M, Rahimian H (2024) Ranking and contextual selection. Oper. Res., ePub ahead of print October 3, https://doi.org/10.1287/opre.2023.0378.Google Scholar
• Kolmogorov AN, Shiryaev AN (1992) Selected Works of A. N. Kolmogorov: Volume 2, Probability Theory and Mathematical Statistics (Kluwer Academic Publishers, Berlin, Heidelberg).Google Scholar
• Kulis B (2013) Metric learning: A survey. Foundations Trends Machine Learn. 5(4):287–364.Google Scholar
• Laidler G, Morgan LE, Nelson BL, Pavlidis NG (2020) Metric learning for simulation analytics. Bae KH, Feng B, Kim S, Lazarova-Molnar S, Zheng Z, Roeder T, Thiesing R, eds. Proc. 2020 Winter Simulation Conf. (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 349–360.Google Scholar
• Law AM, Kelton WD (2007) Simulation Modeling and Analysis. 3rd ed. (McGraw-Hill, New York).Google Scholar
• Li D, Tian Y (2018) Survey and experimental study on metric learning methods. Neural Networks 105(C):447–462.Google Scholar
• Lin Y, Nelson BL, Pei L (2019) Virtual statistics in simulation via k nearest neighbors. INFORMS J. Comput. 31(3):576–592.Link, Google Scholar
• Mahalanobis P (1936) On the generalised distance in statistics. Proc. Natl. Inst. Sci. India 2:49–55.Google Scholar
• McFee B, Lanckriet GR (2010) Metric learning to rank. Fürnkranz J, Joachims T, eds. Proc. 27th Internat. Conf. Machine Learn. (Omnipress, Madison, WI), 775–782.Google Scholar
• Morgan LE, Barton RR (2022) Fourier trajectory analysis for system discrimination. Eur. J. Oper. Res. 296(1):203–217.Google Scholar
• Nelson BL (2016) Some tactical problems in digital simulation’ for the next 10 years. J. Simulation 10(1):2–11.Google Scholar
• Nelson BL, Pei L (2021) Foundations and Methods of Stochastic Simulation: A First Course, 2nd ed. (Springer Nature, Cham, Swizerland).Google Scholar
• Ouyang H, Nelson BL (2017) Simulation-based predictive analytics for dynamic queueing systems. Chan WKV, D’Ambrogio A, Zacharewicz G, Mustafee N, Wainer G, Page E, eds. Proc. 2017 Winter Simulation Conf. (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 1716–1727.Google Scholar
• Peltonen J, Kaski S (2005) Discriminative components of data. IEEE Trans. Neural Networks 16(1):68–83.Google Scholar
• Shannon CE (1948) A mathematical theory of communication. Bell System Tech. J. 27(3):379–423.Google Scholar
• Shapiro A, Dentcheva D, Ruszczyński A (2021) Lectures on Stochastic Programming: Modeling and Theory (Society for Industrial and Applied Mathematics, Philadelphia).Google Scholar
• Shen H, Hong LJ, Zhang X (2021) Ranking and selection with covariates for personalized decision making. INFORMS J. Comput. 33(4):1500–1519.Abstract, Google Scholar
• Suárez JL, García S, Herrera F (2021) A tutorial on distance metric learning: Mathematical foundations, algorithms, experimental analysis, prospects and challenges. Neurocomputing 425:300–322.Google Scholar
• Suo Q, Ma F, Yuan Y, Huai M, Zhong W, Gao J, Zhang A (2018) Deep patient similarity learning for personalized healthcare. IEEE Trans. Nanobioscience 17(3):219–227.Google Scholar
• Tarlow D, Swersky K, Charlin L, Sutskever I, Zemel R (2013) Stochastic k-neighborhood selection for supervised and unsupervised learning. Dasgupta S, McAllester D, eds. Proc. 30th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 28 (JMLR.org), 199–207.Google Scholar
• Torresani L, Lee K (2007) Large margin component analysis. Schölkopf B, Platt JC, Hoffman T, eds. Advances in Neural Information Processing Systems, vol. 19 (MIT Press, Cambridge, MA), 1385–1392.Google Scholar
• Urquhart M, Ljungskog E, Sebben S (2020) Surrogate-based optimisation using adaptively scaled radial basis functions. Appl. Soft Comput. 88(C):106050.Google Scholar
• Vaughan JW (2018) Making better use of the crowd: How crowdsourcing can advance machine learning research. J. Machine Learn. Res. 18(193):1–46.Google Scholar
• Wang D, Tan X (2014) Robust distance metric learning in the presence of label noise. Proc. 28th AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 1321–1327.Google Scholar
• Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. J. Machine Learn. Res. 10(9):207–244.Google Scholar
• Weinberger KQ, Tesauro G (2007) Metric learning for kernel regression. Meila M, Shen X, eds. Proc. 11th Internat. Conf. Artificial Intelligence Statist., Proceedings of Machine Learning Research, vol. 2 (JMLR.org), 612–619.Google Scholar
• Xie P, Wu W, Zhu Y, Xing E (2018) Orthogonality-promoting distance metric learning: Convex relaxation and theoretical analysis. Dy J, Krause A, eds. Proc. 35th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 80 (JMLR.org), 5403–5412.Google Scholar
• Xing EP, Ng AY, Jordan MI, Russell S (2002) Distance metric learning, with application to clustering with side-information. Becker S, Thrun S, Obermayer K, eds. Advances in Neural Information Processing Systems, vol. 15 (MIT Press, Cambridge, MA), 521–528.Google Scholar
• Yang X (2020) Essays on distance metric learning. Unpublished PhD thesis, University College London, London.Google Scholar