SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1721–1730 of 3304 papers

TitleStatusHype
Optimal Projections for Classification with Naive Bayes—0
Optimal Sparse Singular Value Decomposition for High-dimensional High-order Data—0
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters—0
Optimal terminal dimensionality reduction in Euclidean space—0
Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing—0
Optimization of embeddings storage for RAG systems using quantization and dimensionality reduction techniques—0
Optimized Projection for Sparse Representation Based Classification—0
Optimizing Feature Selection with Genetic Algorithms: A Review of Methods and Applications—0
Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition—0
Optimizing Offer Sets in Sub-Linear Time—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