SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1131–1140 of 3304 papers

TitleStatusHype
Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder—0
A Survey on Design-space Dimensionality Reduction Methods for Shape Optimization—0
Demixed Principal Component Analysis—0
A Survey on Archetypal Analysis—0
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects—0
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations—0
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems—0
A Dashboard to Analysis and Synthesis of Dimensionality Reduction Methods in Remote Sensing—0
Accelerating Text Mining Using Domain-Specific Stop Word Lists—0
Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy—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