Stabilizing and Simplifying Sharpened Dimensionality Reduction Using Deep Learning

Author:

Espadoto MateusORCID,Kim Youngjoo,Trager Scott C.,Roerdink Jos B. T. M.,Telea Alexandru C.

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Networks and Communications,Computer Graphics and Computer-Aided Design,Computational Theory and Mathematics,Artificial Intelligence,General Computer Science

Reference73 articles.

1. Liu S, Maljovec D, Wang B, Bremer P-T, Pascucci V. Visualizing high-dimensional data: advances in the past decade. IEEE TVCG. 2015;23(3):1249–68.

2. Nonato L, Aupetit M. Multidimensional projection for visual analytics: Linking techniques with distortions, tasks, and layout enrichment. IEEE TVCG. 2018. https://doi.org/10.1109/TVCG.2018.2846735.

3. Espadoto M, Martins R, Kerren A, Hirata N, Telea A. Toward a quantitative survey of dimension reduction techniques. IEEE TVCG. 2019;27(3):2153–73.

4. Maaten L, Hinton G. Visualizing data using t-SNE. JMLR. 2008;9:2579–605.

5. McInnes L, Healy J. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426v1 [stat.ML] 2018.

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