SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 251–260 of 3304 papers

TitleStatusHype
Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction—0
Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales—0
Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reductionCode0
Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response—0
Quantum Cognition Machine Learning for Forecasting Chromosomal Instability—0
Learning Treatment Representations for Downstream Instrumental Variable Regression—0
Localizing Persona Representations in LLMs—0
Bayesian Data Sketching for Varying Coefficient Regression Models—0
Riemannian Principal Component Analysis—0
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures—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