SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 211–220 of 3304 papers

TitleStatusHype
Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning ApproachCode0
Generalizable Spectral Embedding with an Application to UMAP—0
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting—0
Enhancing UAV Path Planning Efficiency Through Accelerated Learning—0
Exploring Narrative Clustering in Large Language Models: A Layerwise Analysis of BERT—0
Refusal Behavior in Large Language Models: A Nonlinear PerspectiveCode0
PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
Quantum Annealing for Robust Principal Component AnalysisCode0
Discovery of sustainable energy materials via the machine-learned material space—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