SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1431–1440 of 3304 papers

TitleStatusHype
A Correspondence Analysis Framework for Author-Conference Recommendations—0
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft—0
G-invariant diffusion maps—0
GleanVec: Accelerating vector search with minimalist nonlinear dimensionality reduction—0
FlowBERT: Prompt-tuned BERT for variable flow field prediction—0
Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies—0
Global explainability of a deep abstaining classifier—0
Global Sensitivity Analysis of High Dimensional Neuroscience Models: An Example of Neurovascular Coupling—0
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks—0
Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows—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