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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 2701–2710 of 3304 papers

Title	Date	Tasks	Status	Hype
Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection	Sep 25, 2017	Dimensionality Reductionfeature selection	CodeCode Available	0
Tensor-Based Classifiers for Hyperspectral Data Analysis	Sep 24, 2017	ClassificationDimensionality Reduction	—Unverified	0
On Principal Components Regression, Random Projections, and Column Subsampling	Sep 23, 2017	Dimensionality Reductionregression	—Unverified	0
Lazy stochastic principal component analysis	Sep 21, 2017	Dimensionality Reduction	CodeCode Available	0
Near Optimal Sketching of Low-Rank Tensor Regression	Sep 20, 2017	Dimensionality Reductionregression	—Unverified	0
Representation Learning on Graphs: Methods and Applications	Sep 17, 2017	BIG-bench Machine LearningDimensionality Reduction	—Unverified	0
Bayesian nonparametric Principal Component Analysis	Sep 17, 2017	ClusteringDimensionality Reduction	—Unverified	0
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems	Sep 15, 2017	Causal DiscoveryDimensionality Reduction	—Unverified	0
Transform Invariant Auto-encoder	Sep 12, 2017	Dimensionality ReductionPosition	—Unverified	0
Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images	Sep 9, 2017	Classification Of Hyperspectral ImagesDimensionality 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