Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE)-Reference-Cited by-同舟云学术

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Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE)

Published:2021-05 Issue: Volume:40 Page:100378
ISSN:1574-0137
Container-title:Computer Science Review
language:en
Short-container-title:Computer Science Review

Author:

Anowar Farzana,Sadaoui Samira,Selim Bassant^ORCID

Publisher

Elsevier BV

Subject

General Computer Science,Theoretical Computer Science

Reference61 articles.

1. Incremental neural-network learning for big fraud data;Anowar,2020

2. Incremental learning framework for real-world fraud detection environment;Anowar;Comput. Intell.,2021

3. The curse of dimensionality in classification;Spruyt;Comput. Vision Dummies,2014

4. Dimensionality reduction: A comparative review;Van Der Maaten;J. Mach. Learn. Res.,2009

5. A review on dimensionality reduction techniques;Jindal;Int. J. Comput. Appl.,2017

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