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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 2471–2480 of 3304 papers

Title	Date	Tasks	Status	Hype
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting	Oct 1, 2018	ClusteringDimensionality Reduction	—Unverified	0
Quantifying Context Overlap for Training Word Embeddings	Oct 1, 2018	Dimensionality ReductionLanguage Modeling	—Unverified	0
Modeling Dynamics of Biological Systems with Deep Generative Neural Networks	Sep 27, 2018	Dimensionality Reduction	—Unverified	0
Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised Learning	Sep 26, 2018	ClassificationDimensionality Reduction	CodeCode Available	0
Graph filtering for data reduction and reconstruction	Sep 25, 2018	Dimensionality Reduction	—Unverified	0
A convex formulation for high-dimensional sparse sliced inverse regression	Sep 17, 2018	Dimensionality Reductionregression	—Unverified	0
Visualization of High-dimensional Scalar Functions Using Principal Parameterizations	Sep 11, 2018	Dimensionality ReductionSensitivity	CodeCode Available	0
Randomized Iterative Algorithms for Fisher Discriminant Analysis	Sep 9, 2018	Dimensionality Reduction	—Unverified	0
Distance preserving model order reduction of graph-Laplacians and cluster analysis	Sep 9, 2018	ClusteringDimensionality Reduction	—Unverified	0
Mixtures of Skewed Matrix Variate Bilinear Factor Analyzers	Sep 7, 2018	ClusteringDimensionality Reduction	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