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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3051–3060 of 3304 papers

Title	Date	Tasks	Status	Hype
Learning Fair Representations for Kernel Models	Jun 27, 2019	Dimensionality ReductionFairness	CodeCode Available	0
Learning Feature Sparse Principal Subspace	Dec 1, 2020	Dimensionality Reductionfeature selection	CodeCode Available	0
Adaptive Weighted Nonnegative Matrix Factorization for Robust Feature Representation	Jun 7, 2022	Dimensionality Reduction	CodeCode Available	0
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals	Oct 12, 2018	Dimensionality ReductionFault Detection	CodeCode Available	0
Number Representations in LLMs: A Computational Parallel to Human Perception	Feb 22, 2025	Dimensionality Reduction	CodeCode Available	0
Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review	May 7, 2019	ClusteringDimensionality Reduction	CodeCode Available	0
Vector Diffusion Maps and the Connection Laplacian	Feb 1, 2011	Dimensionality Reduction	CodeCode Available	0
Learning from the past, predicting the statistics for the future, learning an evolving system	Sep 1, 2013	Dimensionality Reductionregression	CodeCode Available	0
Deep Continuous Clustering	Mar 5, 2018	ClusteringDimensionality Reduction	CodeCode Available	0
Numerical simulation, clustering and prediction of multi-component polymer precipitation	Jul 10, 2020	BIG-bench Machine LearningClustering	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