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 581–590 of 3304 papers

TitleStatusHype
TopoMap++: A faster and more space efficient technique to compute projections with topological guarantees—0
Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification—0
Applications of machine learning to predict seasonal precipitation for East Africa—0
Optimal Projections for Classification with Naive Bayes—0
Representational Analysis of Binding in Language Models—0
The Surprising Robustness of Partial Least Squares—0
Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models—0
Bridging Autoencoders and Dynamic Mode Decomposition for Reduced-order Modeling and Control of PDEs—0
Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learning—0
Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration—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