References#

[1] Christopher M Bishop, Geoffrey E Hinton, and Iain GD Strachan. Gtm through time. 1997.

[2] Christopher M Bishop, Markus Svens´en, and Christopher KI Williams. Developments of the gener￾ative topographic mapping. Neurocomputing, 21(1-3):203–224, 1998.

[3] Christopher M Bishop, Markus Svens´en, and Christopher KI Williams. Gtm: The generative topo￾graphic mapping. Neural computation, 10(1):215–234, 1998.

[4] Leo Breiman. Random forests. Machine learning, 45(1):5–32, 2001.

[5] Arthur P Dempster, Nan M Laird, and Donald B Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1):1–22, 1977.

[6] Dheeru Dua and Casey Graff. UCI machine learning repository, 2017.

[7] HA Gaspar, II Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek. Gtm-based qsar models and their applicability domains. Molecular informatics, 34(6-7):348–356, 2015.

[8] H´el´ena A Gaspar, Gilles Marcou, Dragos Horvath, Alban Arault, Sylvain Lozano, Philippe Vayer, and Alexandre Varnek. Generative topographic mapping-based classification models and their appli￾cability domain: Application to the biopharmaceutics drug disposition classification system (bddcs). Journal of chemical information and modeling, 53(12):3318–3325, 2013.

[9] H´el´ena Alexandra Gaspar. ugtm: A python package for data modeling and visualization using generative topographic mapping. Journal of Open Research Software, 6(1), 2018.

[10] Rakia Jaziri, Faicel Chamroukhi, Mustapha Lebbah, and Youn`es Bennani. Gtm mixture through time for sequential data. In 2016 International Joint Conference on Neural Networks (IJCNN), pages 2805–2810. IEEE, 2016.

[11] Nathalie Kireeva, II Baskin, HA Gaspar, Dragos Horvath, Gilles Marcou, and Alexandre Varnek. Generative topographic mapping (gtm): universal tool for data visualization, structure-activity mod￾eling and dataset comparison. Molecular informatics, 31(3-4):301–312, 2012.

[12] Teuvo Kohonen. The self-organizing map. Proceedings of the IEEE, 78(9):1464–1480, 1990.

[13] Leland McInnes, John Healy, and James Melville. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426, 2018.

[14] Ian Nabney. NETLAB: algorithms for pattern recognition. Springer Science & Business Media, 2002.

[15] Iv´an Olier and Alfredo Vellido. Advances in clustering and visualization of time series using gtm through time. Neural networks, 21(7):904–913, 2008.

[16] Iv´an Olier, Alfredo Vellido, and Jes´us Giraldo. Kernel generative topographic mapping. In ESANN, volume 2010, pages 481–486. Citeseer, 2010.

[17] Filippo Palumbo, Claudio Gallicchio, Rita Pucci, and Alessio Micheli. Human activity recognition using multisensor data fusion based on reservoir computing. Journal of Ambient Intelligence and Smart Environments, 8(2):87–107, 2016.

[18] Fabian Pedregosa, Ga¨el Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830, 2011.

[19] Lawrence R Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257–286, 1989.

[20] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008.

[21] Alfredo Vellido. Missing data imputation through gtm as a mixture of t-distributions. Neural Networks, 19(10):1624–1635, 2006.