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Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

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Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 211–220 of 3304 papers

Title	Date	Tasks	Status	Hype
Text Classification Algorithms: A Survey	Apr 17, 2019	BIG-bench Machine LearningClassification	CodeCode Available	1
Learning the dynamics of technical trading strategies	Mar 6, 2019	Dimensionality ReductionTime Series	CodeCode Available	1
Deep active subspaces - a scalable method for high-dimensional uncertainty propagation	Feb 27, 2019	Dimensionality ReductionUncertainty Quantification	CodeCode Available	1
catch22: CAnonical Time-series CHaracteristics	Jan 29, 2019	ClassificationClustering	CodeCode Available	1
Unsupervised speech representation learning using WaveNet autoencoders	Jan 25, 2019	Acoustic Unit DiscoveryDecoder	CodeCode Available	1
CatBoost: gradient boosting with categorical features support	Oct 24, 2018	ClusteringCPU	CodeCode Available	1
Hartley Spectral Pooling for Deep Learning	Oct 7, 2018	Deep LearningDimensionality Reduction	CodeCode Available	1
ManifoldNet: A Deep Network Framework for Manifold-valued Data	Sep 11, 2018	Dimensionality Reduction	CodeCode Available	1
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction	Feb 9, 2018	BIG-bench Machine LearningDimensionality Reduction	CodeCode Available	1
Learning Wasserstein Embeddings	Oct 20, 2017	DecoderDimensionality Reduction	CodeCode Available	1

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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