SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1801–1810 of 3304 papers

TitleStatusHype
Predicting Molecular Phenotypes with Single Cell RNA Sequencing Data: an Assessment of Unsupervised Machine Learning Models—0
Prediction of Auto Insurance Risk Based on t-SNE Dimensionality Reduction—0
Prediction of daily maximum ozone levels using Lasso sparse modeling method—0
Prediction with Dimension Reduction of Multiple Molecular Data Sources for Patient Survival—0
Predictive Liability Models and Visualizations of High Dimensional Retail Employee Data—0
Preliminary Steps Towards Federated Sentiment Classification—0
Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic Optimization—0
Preserving clusters and correlations: a dimensionality reduction method for exceptionally high global structure preservation—0
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy—0
PRHLT: Combination of Deep Autoencoders with Classification and Regression Techniques for SemEval-2015 Task 11—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