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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 2571–2580 of 3304 papers

Title	Date	Tasks	Status	Hype
Mathematical Analysis on Out-of-Sample Extensions	Apr 19, 2018	Dimensionality Reduction	—Unverified	0
Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification	Apr 19, 2018	Dimensionality ReductionGeneral Classification	—Unverified	0
varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets	Apr 19, 2018	Dimensionality Reduction	—Unverified	0
Deep Embedding Kernel	Apr 16, 2018	Deep LearningDimensionality Reduction	—Unverified	0
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features	Apr 13, 2018	Anomaly DetectionDecision Making	CodeCode Available	0
Online Multi-Label Classification: A Label Compression Method	Apr 4, 2018	ClassificationDimensionality Reduction	—Unverified	0
Robust Subspace Clustering with Compressed Data	Mar 30, 2018	ClusteringComputational Efficiency	—Unverified	0
Canonical Correlation Analysis of Datasets with a Common Source Graph	Mar 27, 2018	ClusteringDimensionality Reduction	—Unverified	0
Reservoir computing approaches for representation and classification of multivariate time series	Mar 21, 2018	Dimensionality ReductionGeneral Classification	CodeCode Available	0
Sparse Reduced Rank Regression With Nonconvex Regularization	Mar 20, 2018	Dimensionality ReductionEconometrics	—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