SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 321–330 of 3304 papers

TitleStatusHype
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections—0
Adaptive Locally Linear Embedding—0
Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models—0
Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks—0
econSG: Efficient and Multi-view Consistent Open-Vocabulary 3D Semantic Gaussians—0
Machine Learning Reveals Composition Dependent Thermal Stability in Halide Perovskites—0
Early detection of diabetes through transfer learning-based eye (vision) screening and improvement of machine learning model performance and advanced parameter setting algorithms—0
Dimensionality reduction for k-means clustering of large-scale influenza mutation datasets—0
Grammar-based Ordinary Differential Equation Discovery—0
Measuring the Data—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