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 251–260 of 3304 papers

TitleStatusHype
Nested Diffusion Models Using Hierarchical Latent Priors—0
A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing—0
An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction—0
Deep Learning, Machine Learning, Advancing Big Data Analytics and Management—0
Traversing the Subspace of Adversarial Patches—0
Enhancing the conformal predictability of context-aware recommendation systems by using Deep Autoencoders—0
Noncommutative Model Selection for Data Clustering and Dimension Reduction Using Relative von Neumann Entropy—0
Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling—0
Autoencoder Enhanced Realised GARCH on Volatility Forecasting—0
HiCat: A Semi-Supervised Approach for Cell Type Annotation—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