SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 3071–3080 of 3304 papers

TitleStatusHype
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses—0
Deep Variational Sufficient Dimensionality Reduction—0
Defining Reference Sequences for Nocardia Species by Similarity and Clustering Analyses of 16S rRNA Gene Sequence Data—0
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations—0
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects—0
Demixed Principal Component Analysis—0
Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder—0
Demystifying Embedding Spaces using Large Language Models—0
DenDrift: A Drift-Aware Algorithm for Host Profiling—0
Denoising VAE as an Explainable Feature Reduction and Diagnostic Pipeline for Autism Based on Resting state fMRI—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