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Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

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Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3271–3280 of 3304 papers

Title	Date	Tasks	Status	Hype
Hidden Talents of the Variational Autoencoder	Jun 16, 2017	Dimensionality Reduction	CodeCode Available	0
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning	Feb 19, 2024	Dimensionality ReductionFederated Learning	CodeCode Available	0
Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning Approach	Jan 20, 2025	Dimensionality ReductionFeature Engineering	CodeCode Available	0
Matrix factorisation and the interpretation of geodesic distance	Jun 2, 2021	Dimensionality Reduction	CodeCode Available	0
A Generalized EigenGame with Extensions to Multiview Representation Learning	Nov 21, 2022	Dimensionality ReductionRepresentation Learning	CodeCode Available	0
Towards aerodynamic surrogate modeling based on β-variational autoencoders	Aug 9, 2024	DecoderDimensionality Reduction	CodeCode Available	0
Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political Parties	Jul 9, 2020	Contrastive LearningDimensionality Reduction	CodeCode Available	0
Analysis of Trade-offs in Fair Principal Component Analysis Based on Multi-objective Optimization	Jun 11, 2020	Dimensionality ReductionFairness	CodeCode Available	0
Parametric generation of conditional geological realizations using generative neural networks	Jul 13, 2018	Dimensionality Reduction	CodeCode Available	0
A Statistical View of Column Subset Selection	Jul 24, 2023	Dimensionality Reduction	CodeCode Available	0

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All datasets MCA EMNIST

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	UDRN	Classification Accuracy	90.9	—	Unverified
2	tSNE	Classification Accuracy	51.5	—	Unverified
3	IVIS	Classification Accuracy	46.6	—	Unverified
4	UMAP	Classification Accuracy	41.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UDRN	Classification Accuracy	71.1	—	Unverified
2	QS	Classification Accuracy	68	—	Unverified