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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 2521–2530 of 3304 papers

Title	Date	Tasks	Status	Hype
Overlapping Sliced Inverse Regression for Dimension Reduction	Jun 23, 2018	Dimensionality Reductionregression	—Unverified	0
Parallel Transport Unfolding: A Connection-based Manifold Learning Approach	Jun 23, 2018	Dimensionality Reduction	—Unverified	0
Virtual Codec Supervised Re-Sampling Network for Image Compression	Jun 22, 2018	Dimensionality ReductionImage Compression	—Unverified	0
Generalizing Correspondence Analysis for Applications in Machine Learning	Jun 21, 2018	BIG-bench Machine LearningDimensionality Reduction	—Unverified	0
A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors	Jun 19, 2018	Dimensionality Reduction	CodeCode Available	0
Diffeomorphic brain shape modelling using Gauss-Newton optimisation	Jun 19, 2018	Dimensionality Reduction	—Unverified	0
BinGAN: Learning Compact Binary Descriptors with a Regularized GAN	Jun 18, 2018	Dimensionality ReductionRetrieval	CodeCode Available	0
Introducing user-prescribed constraints in Markov chains for nonlinear dimensionality reduction	Jun 13, 2018	Dimensionality Reduction	CodeCode Available	0
DG-GL: Differential geometry based geometric learning of molecular datasets	Jun 11, 2018	DescriptiveDimensionality Reduction	—Unverified	0
Smart Analytical Signature Verification For DSP Applications	Jun 10, 2018	Dimensionality Reduction	—Unverified	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