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 451–460 of 3304 papers

TitleStatusHype
Noisy Data Visualization using Functional Data Analysis—0
The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty QuantificationCode0
Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective—0
Random Subspace Local Projections—0
VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantificationCode1
A comparison of correspondence analysis with PMI-based word embedding methodsCode0
Estimates on the domain of validity for Lyapunov-Schmidt reduction—0
Enhancing Sufficient Dimension Reduction via Hellinger CorrelationCode0
Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach—0
On the Connection Between Non-negative Matrix Factorization and Latent Dirichlet Allocation—0
Show:102550
← PrevPage 46 of 331Next →
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