Generalized Low-Computational Cost Laplacian Eigenmaps-Reference-Cited by-同舟云学术

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Generalized Low-Computational Cost Laplacian Eigenmaps

Published:2018 Issue: Volume: Page:661-669
ISSN:0302-9743
Container-title:Intelligent Data Engineering and Automated Learning – IDEAL 2018
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Author:

Salazar-Castro J. A.,Peña D. F.,Basante C.,Ortega C.,Cruz-Cruz L.,Revelo-Fuelagán J.,Blanco-Valencia X. P.,Castellanos-Domínguez G.,Peluffo-Ordóñez D. H.

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-03493-1_69

Reference15 articles.

1. Borg, I., Groenen, P.J.: Modern Multidimensional Scaling: Theory and Applications. Springer, New York (2005). https://doi.org/10.1007/0-387-28981-X

2. Salazar-Castro, J.A., et al.: Dimensionality reduction for interactive data visualization via a geo-desic approach. In: 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp. 1–6. IEEE (2016)

3. Lecture Notes in Computer Science;JA Salazar-Castro,2018

4. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)

5. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)

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1. Interactive Information Visualization Models: A Systematic Literature Review;Computational Science and Its Applications – ICCSA 2023;2023

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