SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 601–610 of 3304 papers

TitleStatusHype
An Introduction to Autoencoders—0
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines—0
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication—0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
An Innovative Imputation and Classification Approach for Accurate Disease Prediction—0
A Fuzzy Approach for Feature Evaluation and Dimensionality Reduction to Improve the Quality of Web Usage Mining Results—0
A Functional approach for Two Way Dimension Reduction in Time Series—0
A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics—0
Clustering high dimensional meteorological scenarios: results and performance index—0
Clustering, Classification, Discriminant Analysis, and Dimension Reduction via Generalized Hyperbolic Mixtures—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