SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2331–2340 of 3304 papers

TitleStatusHype
Deep incremental learning models for financial temporal tabular datasets with distribution shifts—0
Relating tSNE and UMAP to Classical Dimensionality Reduction—0
Unified dimensionality reduction techniques in chronic liver disease detection—0
Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey—0
Uniform Approximations for Randomized Hadamard Transforms with Applications—0
Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Survey—0
Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis—0
Universality laws for randomized dimension reduction, with applications—0
Universality of macroscopic neuronal dynamics in Caenorhabditis elegans—0
Unlocking NACE Classification Embeddings with OpenAI for Enhanced Analysis and Processing—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