SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 371–380 of 3304 papers

TitleStatusHype
Representational Analysis of Binding in Language Models—0
Optimal Projections for Classification with Naive Bayes—0
Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models—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
Nearest Neighbor CCP-Based Molecular Sequence Analysis—0
A semi-supervised learning using over-parameterized regression—0
Optimizing Feature Selection with Genetic Algorithms: A Review of Methods and Applications—0
Application Research On Real-Time Perception Of Device Performance Status—0
The R package psvmSDR: A Unified Algorithm for Sufficient Dimension Reduction via Principal Machines—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