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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 191–200 of 3304 papers

Title	Date	Tasks	Status	Hype
Renormalized Mutual Information for Artificial Scientific Discovery	May 4, 2020	Dimensionality Reductionscientific discovery	CodeCode Available	1
On time series representations for multi-label NILM	May 2, 2020	Dimensionality ReductionNon-Intrusive Load Monitoring	CodeCode Available	1
Sign Bits Are All You Need for Black-Box Attacks	May 1, 2020	Adversarial AttackAll	CodeCode Available	1
BayesOpt Adversarial Attack	May 1, 2020	Adversarial AttackBayesian Optimisation	CodeCode Available	1
Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!	Apr 30, 2020	ClusteringDimensionality Reduction	CodeCode Available	1
Flows for simultaneous manifold learning and density estimation	Mar 31, 2020	DenoisingDensity Estimation	CodeCode Available	1
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection	Mar 11, 2020	Dimensionality ReductionFraud Detection	CodeCode Available	1
Supervised Domain Adaptation using Graph Embedding	Mar 9, 2020	Dimensionality ReductionDomain Adaptation	CodeCode Available	1
BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders	Mar 6, 2020	ClusteringDecoder	CodeCode Available	1
Dimensionality reduction to maximize prediction generalization capability	Mar 1, 2020	Dimensionality ReductionPrediction	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