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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 1591–1600 of 3304 papers

Title	Date	Tasks	Status	Hype
Multidimensional Scaling for Gene Sequence Data with Autoencoders	Apr 19, 2021	Dimensionality Reduction	—Unverified	0
Grassmann Iterative Linear Discriminant Analysis with Proxy Matrix Optimization	Apr 16, 2021	Dimensionality Reduction	—Unverified	0
Capturing patterns of variation unique to a specific dataset	Apr 16, 2021	Dimensionality Reduction	CodeCode Available	0
Jointly Modeling and Clustering Tensors in High Dimensions	Apr 15, 2021	BenchmarkingClustering	—Unverified	0
Unsupervised low-rank representations for speech emotion recognition	Apr 14, 2021	Dimensionality ReductionEmotion Recognition	—Unverified	0
Classifying herbal medicine origins by temporal and spectral data mining of electronic nose	Apr 14, 2021	Dimensionality Reduction	CodeCode Available	0
Revisiting Bayesian Autoencoders with MCMC	Apr 13, 2021	Bayesian InferenceDeep Learning	CodeCode Available	0
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap	Apr 12, 2021	Dimensionality Reduction	—Unverified	0
Adversarially-Trained Nonnegative Matrix Factorization	Apr 10, 2021	Dimensionality ReductionMatrix Completion	CodeCode Available	0
Granger Causality Based Hierarchical Time Series Clustering for State Estimation	Apr 9, 2021	ClusteringDimensionality 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