SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1251–1260 of 3304 papers

TitleStatusHype
Covariance matrix preparation for quantum principal component analysis—0
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems—0
Knowledge Base Index Compression via Dimensionality and Precision ReductionCode0
Correlation-based feature selection to identify functional dynamics in proteinsCode1
Nearly minimax robust estimator of the mean vector by iterative spectral dimension reduction—0
An efficient real-time target tracking algorithm using adaptive feature fusion—0
Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting—0
The Fast Johnson-Lindenstrauss Transform is Even FasterCode0
MLPro: A System for Hosting Crowdsourced Machine Learning Challenges for Open-Ended Research Problems—0
Path Development Network with Finite-dimensional Lie Group RepresentationCode1
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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