SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2791–2800 of 3304 papers

TitleStatusHype
Neural method for Explicit Mapping of Quasi-curvature Locally Linear Embedding in image retrieval—0
Joint Embedding of GraphsCode0
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy—0
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction—0
Applying Ricci Flow to High Dimensional Manifold Learning—0
The Unreasonable Effectiveness of Structured Random Orthogonal EmbeddingsCode1
L^3-SVMs: Landmarks-based Linear Local Support Vectors Machines—0
A description length approach to determining the number of k-means clusters—0
Semi-supervised Learning based on Distributionally Robust Optimization—0
Feasibility of Principal Component Analysis in hand gesture recognition system—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