SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2361–2370 of 3304 papers

TitleStatusHype
Minimax-Optimal Dimension-Reduced Clustering for High-Dimensional Nonspherical Mixtures—0
Minimum description length as an objective function for non-negative matrix factorization—0
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression—0
Mixed Multi-Model Semantic Interaction for Graph-based Narrative Visualizations—0
Mixed-Variate Restricted Boltzmann Machines—0
Mixtape Application: Music Map Methodology and Evaluation—0
Mixture Probabilistic Principal Geodesic Analysis—0
MKL-RT: Multiple Kernel Learning for Ratio-trace Problems via Convex Optimization—0
MLC Toolbox: A MATLAB/OCTAVE Library for Multi-Label Classification—0
ML-Driven Approaches to Combat Medicare Fraud: Advances in Class Imbalance Solutions, Feature Engineering, Adaptive Learning, and Business Impact—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