V. Hodge and J. Austin, A survey of outlier detection methodologies, Artificial intelligence review, vol.22, issue.2, pp.85-126, 2004.

M. Annick, . Leroy, J. Peter, and . Rousseeuw, Robust regression and outlier detection, Wiley Series in Probability and Mathematical Statistics, 1987.

R. Zuriana-abu-bakar and . Mohemad, A comparative study for outlier detection techniques in data mining, IEEE CCIS, pp.1-6, 2006.

M. Markou and S. Singh, Novelty detection: a review-part 1: statistical approaches, vol.83, pp.2481-2497, 2003.

M. Abdel-sayed, D. Duclos, G. Faÿ, J. Lacaille, and M. Mougeot, Dictionary comparison for anomaly detection on aircraft engine spectrograms, Machine Learning and Data Mining in Pattern Recognition, pp.362-376, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02526568

E. Keogh, S. Lonardi, and B. Chiu, Finding surprising patterns in a time series database in linear time and space, ACM SIGKDD, pp.550-556, 2002.

R. Fujimaki, T. Yairi, and K. Machida, An approach to spacecraft anomaly detection problem using kernel feature space, Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp.401-410, 2005.

M. Larry, M. Manevitz, and . Yousef, One-class svms for document classification, Journal of machine Learning research, vol.2, pp.139-154, 2001.

J. Ma and S. Perkins, Time-series novelty detection using one-class support vector machines, IJCNN, vol.3, pp.1741-1745, 2003.

M. Markus, H. Breunig, R. T. Kriegel, J. Ng, and . Sander, Lof: identifying density-based local outliers, ACM Sigmod Record, vol.29, pp.93-104, 2000.

H. Ma, Y. Hu, and H. Shi, Fault detection and identification based on the neighborhood standardized local outlier factor method, Industrial & Engineering Chemistry Research, vol.52, issue.6, pp.2389-2402, 2013.

M. Salehi, C. Leckie, C. James, T. Bezdek, X. Vaithianathan et al., Fast memory efficient local outlier detection in data streams, IEEE Transactions on Knowledge and Data Engineering, vol.28, issue.12, pp.3246-3260, 2016.

Z. Chen, K. Xu, J. Wei, and G. Dong, Voltage fault detection for lithium-ion battery pack using local outlier factor, Measurement, 2019.

T. Fei, K. M. Liu, Z. Ting, and . Zhou, Isolation forest, IEEE Conf. on Data Mining, pp.413-422, 2008.

C. Villani, Optimal transport: old and new, vol.338, 2008.

G. Peyré and M. Cuturi, Computational optimal transport, 2017.

M. Cuturi, Sinkhorn distances: Lightspeed computation of optimal transport, Advances in neural information processing systems, pp.2292-2300, 2013.

A. Genevay, M. Cuturi, G. Peyré, and F. Bach, Stochastic optimization for large-scale optimal transport, Advances in Neural Information Processing Systems, pp.3440-3448, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01321664

A. Alaoui-belghiti, S. Chevallier, and E. Monacelli, Unsupervised anomaly detection using optimal transport for predictive maintenance, ICANN, p.tbp, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02541920

S. Chevallier, G. Bao, M. Hammami, F. Marlats, L. Mayaud et al., Brain-machine interface for mechanical ventilation using respiratoryrelated evoked potential, ICANN, pp.662-671, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01911136

A. Rakotomamonjy, V. Guigue, G. Mallet, and V. Alvarado, Ensemble of SVMs for improving braincomputer interface P300 speller performances, 15th International Conference on Artificial Neural Networks, pp.45-50, 2005.

N. Jrad, M. Congedo, R. Phlypo, S. Rousseau, R. Flamary et al., sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces, Journal of Neural Engineering, vol.8, issue.5, p.56004, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00617810