Abstract
Learning in the model space (LiMS) represents each observational unit (e.g. sparse and irregular time series) with a suitable model of it (point estimate), or a full posterior distribution over models. LiMS approaches take the mechanistic information of how the data is generated into account, thus enhancing the transparency and interpretability of the machine learning tools employed. In this paper we develop a novel topographic mapping in the model space and compare it with an extension of the Generative Topographic Mapping (GTM) to the model space. We demonstrate these two methods on a dataset of measurements taken on subjects in an adrenal steroid hormone deficiency study.
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References
Arlt, W., et al.: Steroid metabolome analysis reveals prevalent glucocorticoid excess in primary aldosteronism. JCI Insight 2(8), e93136 (2017)
Arlt, W., Stewart, P.M.: Adrenal corticosteroid biosynthesis, metabolism, and action. Endocrinol. Metab. Clin 34(2), 293–313 (2005)
Bishop, C.M., Svensén, M., Williams, C.K.: Developments of the generative topographic mapping. Neurocomputing 21(1–3), 203–224 (1998)
Bishop, C.M., Svensén, M., Williams, C.K.: GTM: the generative topographic mapping. Neural Comput. 10(1), 215–234 (1998)
Gianniotis, N., Tino, P.: Visualization of tree-structured data through generative topographic mapping. IEEE Trans. Neural Netw. 19(8), 1468–1493 (2008)
Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 4, 580–585 (1985)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69 (1982)
Kohonen, T.: Essentials of the self-organizing map. Neural Netw. 37, 52–65 (2013)
Natita, W., Wiboonsak, W., Dusadee, S.: Appropriate learning rate and neighborhood function of self-organizing map (som) for specific humidity pattern classification over southern thailand. Int. J. Model. Optim. 6(1), 61 (2016)
Ni, H., Yin, H.: A self-organising mixture autoregressive network for fx time series modelling and prediction. Neurocomputing 72(16–18), 3529–3537 (2009)
Rasmussen, C.E., Williams, C.: Gaussian Processes for Machine Learning, vol. 32, p. 68. The Mit Press, Cambridge (2006)
Shen, Y., Tino, P., Tsaneva-Atanasova, K.: Classification framework for partially observed dynamical systems. Phys. Rev. E 95(4), 043303 (2017)
Stefanovič, P., Kurasova, O.: Visual analysis of self-organizing maps. Nonlinear Anal. Model. Control 16(4), 488–504 (2011)
Tino, P., Kabán, A., Sun, Y.: A generative probabilistic approach to visualizing sets of symbolic sequences. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–706 (2004)
Torma, M.: Kohonen self-organizing feature map and its use in clustering. In: ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision, vol. 2357, pp. 830–835. International Society for Optics and Photonics (1994)
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Chen, X. et al. (2021). SOMiMS - Topographic Mapping in the Model Space. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2021. IDEAL 2021. Lecture Notes in Computer Science(), vol 13113. Springer, Cham. https://doi.org/10.1007/978-3-030-91608-4_50
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DOI: https://doi.org/10.1007/978-3-030-91608-4_50
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