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 1361–1370 of 3304 papers

TitleStatusHype
From RGB to Spectrum for Natural Scenes via Manifold-Based Mapping—0
Construction of neural networks for realization of localized deep learning—0
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
A Powerful Face Preprocessing For Robust Kinship Verification based Tensor Analyses—0
From Players to Champions: A Generalizable Machine Learning Approach for Match Outcome Prediction with Insights from the FIFA World Cup—0
Functional Inverse Regression in an Enlarged Dimension Reduction Space—0
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices—0
Functional sufficient dimension reduction through information maximization with application to classification—0
Consistent Representation Learning for High Dimensional Data Analysis—0
Consistent Estimation of Low-Dimensional Latent Structure in High-Dimensional Data—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