SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1471–1480 of 3304 papers

TitleStatusHype
Dimensionality reduction for prediction: Application to Bitcoin and Ethereum—0
Exact Post-selection Inference For Tracking S&P500—0
Uniform-in-Phase-Space Data Selection with Iterative Normalizing FlowsCode0
Nonnegative OPLS for Supervised Design of Filter Banks: Application to Image and Audio Feature Extraction—0
Artificial Intelligence and Dimensionality Reduction: Tools for approaching future communications—0
Machine learning discovery of new phases in programmable quantum simulator snapshotsCode0
Manifold learning via quantum dynamics—0
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction—0
Funnels: Exact maximum likelihood with dimensionality reductionCode0
Risk and optimal policies in bandit experiments—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified