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 2391–2400 of 3304 papers

TitleStatusHype
Mori-Zwanzig latent space Koopman closure for nonlinear autoencoder—0
MPAD: A New Dimension-Reduction Method for Preserving Nearest Neighbors in High-Dimensional Vector Search—0
MRI Patterns of the Hippocampus and Amygdala for Predicting Stages of Alzheimer's Progression: A Minimal Feature Machine Learning Framework—0
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems—0
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case study—0
Multiclass spectral feature scaling method for dimensionality reduction—0
Multi-Criteria Radio Spectrum Sharing With Subspace-Based Pareto Tracing—0
Generating semantic maps through multidimensional scaling: linguistic applications and theory—0
Multidimensional Scaling for Gene Sequence Data with Autoencoders—0
Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations—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