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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 2141–2150 of 3304 papers

Title	Date	Tasks	Status	Hype
Dimension Estimation Using Autoencoders	Sep 24, 2019	Dimensionality Reduction	—Unverified	0
Manifold Fitting under Unbounded Noise	Sep 23, 2019	Dimensionality Reduction	CodeCode Available	0
Application of Fuzzy Clustering for Text Data Dimensionality Reduction	Sep 21, 2019	ClusteringDimensionality Reduction	—Unverified	0
Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation	Sep 20, 2019	Dimensionality ReductionUncertainty Quantification	—Unverified	0
Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs	Sep 20, 2019	Dimensionality Reduction	—Unverified	0
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction	Sep 19, 2019	Dimensionality Reduction	CodeCode Available	1
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold	Sep 19, 2019	Dimensionality Reductionfeature selection	—Unverified	0
Data Mapping and Finite Difference Learning	Sep 18, 2019	Dimensionality Reduction	CodeCode Available	0
Laplacian Matrix for Dimensionality Reduction and Clustering	Sep 18, 2019	ClusteringDimensionality Reduction	—Unverified	0
PixelHop: A Successive Subspace Learning (SSL) Method for Object Classification	Sep 17, 2019	BenchmarkingDecision Making	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