SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 651–660 of 3304 papers

TitleStatusHype
Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities—0
Sparsifying dimensionality reduction of PDE solution data with Bregman learning—0
Oblivious subspace embeddings for compressed Tucker decompositions—0
Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning—0
Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance AnalysisCode0
Macroscopic Market Making Games via Multidimensional Decoupling Field—0
Unsupervised learning of Data-driven Facial Expression Coding System (DFECS) using keypoint tracking—0
VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region AnnotationCode0
Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from SimulationsCode0
Enhancing Supervised Visualization through Autoencoder and Random Forest Proximities for Out-of-Sample Extension—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