SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 201–210 of 3304 papers

TitleStatusHype
OmiEmbed: a unified multi-task deep learning framework for multi-omics dataCode1
Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer CompressionCode1
A local approach to parameter space reduction for regression and classification tasksCode1
Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reductionCode1
Pattern or Artifact? Interactively Exploring Embedding Quality with TRACECode1
Perplexity-free Parametric t-SNECode1
DMT-HI: MOE-based Hyperbolic Interpretable Deep Manifold Transformation for Unspervised Dimensionality ReductionCode1
ProsoBeast Prosody Annotation ToolCode1
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in PythonCode1
Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximationCode1
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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