Generalized Spectral Dimensionality Reduction Based on Kernel Representations and Principal Component Analysis-Reference-Cited by-同舟云学术

Generalized Spectral Dimensionality Reduction Based on Kernel Representations and Principal Component Analysis

Published:2021 Issue: Volume: Page:512-523
ISSN:0302-9743
Container-title:Computational Science and Its Applications – ICCSA 2021
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Author:

Ortega-Bustamante MacArthur C.,Hasperué Waldo,Peluffo-Ordóñez Diego H.,González-Vergara Juan,Marín-Gaviño Josué,Velez-Falconi Martín

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-86973-1_36

Reference15 articles.

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3. Belanche Muñoz, L.A.: Developments in kernel design. In: ESANN 2013 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 24–26 April 2013, pp. 369–378 (2013)

4. Bagchi, A.: Lecture notes: Efficient approximation of kernel functions (2020)

5. Ramon, E., Belanche-Muñoz, L., Molist, F., Quintanilla, R., Perez-Enciso, M., Ramayo-Caldas, Y.: kernint: a kernel framework for integrating supervised and unsupervised analyses in spatio-temporal metagenomic datasets. Front. Microbiol. 12, 60 (2021)

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