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Empirical comparison between autoencoders and ...
arXiv
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arXiv
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由 Q Fournier 著作2021被引用 86 次 — Such networks are called autoencoders and, once trained, yield a non-linear dimensionality reduction that outperforms SVD- based methods.
Empirical Comparison between Autoencoders and ...
IEEE Xplore
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IEEE Xplore
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由 Q Fournier 著作2019被引用 86 次 — This work aims to show that PCA is still a relevant technique for dimensionality reduction in the context of classification.
Empirical Comparison between Autoencoders and ...
IEEE Computer Society
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由 Q Fournier 著作2019被引用 86 次 — This work aims to show that PCA is still a relevant technique for dimensionality reduction in the context of classification.
Empirical comparison between autoencoders and ...
ResearchGate
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ResearchGate
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2024年9月7日 — This work aims to show that PCA is still a relevant technique for dimensionality reduction in the context of classification. To this purpose, we ...
[PDF] Empirical Comparison between Autoencoders and ...
Semantic Scholar
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Semantic Scholar
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Comparative study of methods to obtain the number of hidden neurons of an auto-encoder in a high-dimensionality context · Evaluating Architectures and ...
Empirical Comparison between Autoencoders and ...
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In the article [21] , traditional dimensionality reduction methods such as PCA and Isomap are compared with autoencoders. The study demonstrated the relevance ...
Empirical Comparison between Autoencoders and Traditional ...
PolyPublie
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PolyPublie
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由 Q Fournier 著作2019被引用 86 次 — Empirical Comparison between Autoencoders and Traditional Dimensionality Reduction Methods ; IEEE Second International Conference on Artificial ...
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components versus autoencoders for dimensionality ...
Archive ouverte HAL
https://hal.science › hal-04506004 › document
Archive ouverte HAL
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由 K Mishra 著作2023 — understanding the effect of deep learning and traditional dimensionality reduction ... empirical comparison of dimensionality reduction algorithms.
Dimensionality reduction for images of IoT using machine ...
National Institutes of Health (NIH) (.gov)
https://pmc.ncbi.nlm.nih.gov › articles
National Institutes of Health (NIH) (.gov)
https://pmc.ncbi.nlm.nih.gov › articles
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由 I Ali 著作2024被引用 7 次 — Fournier and Aloise proposed an empirical comparison between autoencoders and traditional dimensionality reduction methods. They evaluated ...
Comparison of Dimensionality Reduction Methods
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2024年8月2日 — 4. RP - introduce some level of approximation in distance calculations, leading to a loss of accuracy, not suitable for all types of data sets.
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