SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2561–2570 of 3304 papers

TitleStatusHype
On the use of Wasserstein metric in topological clustering of distributional data—0
OPDR: Order-Preserving Dimension Reduction for Semantic Embedding of Multimodal Scientific Data—0
Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti—0
Optimal approximate matrix product in terms of stable rank—0
Optimal estimation of sparse topic models—0
Optimal high-precision shadow estimation—0
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform—0
Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measures—0
Optimal learning rates for Kernel Conjugate Gradient regression—0
Optimal Projections for Classification with Naive Bayes—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