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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 2591–2600 of 3304 papers

Title	Date	Tasks	Status	Hype
A more globally accurate dimensionality reduction method using triplets	Mar 1, 2018	Dimensionality ReductionTriplet	CodeCode Available	0
Deep Learning for Causal Inference	Mar 1, 2018	Causal InferenceDeep Learning	—Unverified	0
Autoencoding topology	Mar 1, 2018	BIG-bench Machine LearningClustering	—Unverified	0
Static and Dynamic Robust PCA and Matrix Completion: A Review	Mar 1, 2018	Dimensionality ReductionLow-Rank Matrix Completion	—Unverified	0
Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow	Feb 27, 2018	Dimensionality Reduction	CodeCode Available	0
DID: Distributed Incremental Block Coordinate Descent for Nonnegative Matrix Factorization	Feb 25, 2018	Dimensionality Reduction	—Unverified	0
Improved Regularity Model-based EDA for Many-objective Optimization	Feb 24, 2018	Dimensionality ReductionDiversity	—Unverified	0
Diffusion Maps meet Nyström	Feb 23, 2018	Dimensionality ReductionTime Series	—Unverified	0
AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction	Feb 23, 2018	Dimensionality ReductionGeneral Classification	—Unverified	0
Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation	Feb 23, 2018	Collaborative FilteringDimensionality 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