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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 641–650 of 3304 papers

Title	Date	Tasks	Status	Hype	Score
DiffRed: Dimensionality Reduction guided by stable rank	Mar 9, 2024	Data SummarizationData Visualization	CodeCode Available	0	5
Calibrating dimension reduction hyperparameters in the presence of noise	Dec 5, 2023	Dimensionality Reductionfeature selection	CodeCode Available	0	5
Caffe: Convolutional Architecture for Fast Feature Embedding	Jun 20, 2014	ClusteringDimensionality Reduction	CodeCode Available	0	5
Genomic data analysis in tree spaces	Jul 25, 2016	Dimensionality Reduction	CodeCode Available	0	5
Deep Temporal Clustering: Fully unsupervised learning of time-domain features	Jan 1, 2018	ClusteringDimensionality Reduction	CodeCode Available	0	5
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective	Jun 13, 2025	Anomaly DetectionDimensionality Reduction	CodeCode Available	0	5
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data	Mar 6, 2019	Dimensionality ReductionGaussian Processes	CodeCode Available	0	5
Graph Convolutional Networks Meet with High Dimensionality Reduction	Nov 7, 2019	BenchmarkingDimensionality Reduction	CodeCode Available	0	5
GraphTSNE: A Visualization Technique for Graph-Structured Data	Apr 15, 2019	Dimensionality Reduction	CodeCode Available	0	5
Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features	Feb 4, 2018	ClusteringDimensionality Reduction	CodeCode Available	0	5

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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