SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 31–40 of 3304 papers

TitleStatusHype
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
Pattern or Artifact? Interactively Exploring Embedding Quality with TRACECode1
From Analog to Digital: Multi-Order Digital Joint Coding-Modulation for Semantic CommunicationCode1
VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantificationCode1
CBMAP: Clustering-based manifold approximation and projection for dimensionality reductionCode1
Distributional Principal AutoencodersCode1
scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph EmbeddingCode1
Remote sensing framework for geological mapping via stacked autoencoders and clusteringCode1
Targeted Visualization of the Backbone of Encoder LLMsCode1
Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking SystemsCode1
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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