Impact of data subsamplings in Fast Multi-Scale Neighbor Embedding

Lambert, Pierre;Lee, John;Verleysen, Michel;De Bodt, Cyril
(2021) European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning — Location: Online event (6.October.2021)

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Fast multi-scale neighbor embedding (f-ms-NE) is an algorithm that maps high-dimensional data to a low-dimensional space by preserving the multi-scale data neighborhoods. To lower its time complexity, f-ms-NE uses random subsamplings to estimate the data properties at multiple scales. To improve this estimation and study the f-ms-NE sensitivity to randomness, this paper generalizes the f-ms-NE cost function by averaging several subsamplings. Experiments reveal that this can slightly improve the quality of the embeddings while maintaining reasonable computation times. Codes are available at "https://github.com/cdebodt/Fast_Multi-scale_NE".
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Lambert, P., Lee, J., Verleysen, M., & De Bodt, C. (2021). Impact of data subsamplings in Fast Multi-Scale Neighbor Embedding. ESANN 2021 proceedings, 435-440. https://hdl.handle.net/2078.5/228869