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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 3241–3250 of 3304 papers

Title	Date	Tasks	Status	Hype
Manifold Denoising by Nonlinear Robust Principal Component Analysis	Nov 10, 2019	DenoisingDimensionality Reduction	CodeCode Available	0
A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction	Dec 1, 2020	ClusteringDimensionality Reduction	CodeCode Available	0
K-Deep Simplex: Deep Manifold Learning via Local Dictionaries	Dec 3, 2020	ClusteringDeep Clustering	CodeCode Available	0
Grassmann Stein Variational Gradient Descent	Feb 7, 2022	Dimensionality Reduction	CodeCode Available	0
Gravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General Relativity	Oct 30, 2022	Dimensionality ReductionMetric Learning	CodeCode Available	0
Lazy FSCA for Unsupervised Variable Selection	Mar 3, 2021	Dimensionality ReductionVariable Selection	CodeCode Available	0
Bi-Sparse Unsupervised Feature Selection	Dec 22, 2024	Dimensionality Reductionfeature selection	CodeCode Available	0
GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing	Aug 8, 2019	Dimensionality ReductionDiversity	CodeCode Available	0
An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder	Sep 17, 2020	Dimensionality ReductionGeneral Classification	CodeCode Available	0
Manifold learning in metric spaces	Mar 20, 2025	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