SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 501–510 of 3304 papers

TitleStatusHype
Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design—0
Applying Supervised Learning Algorithms and a New Feature Selection Method to Predict Coronary Artery Disease—0
A Hybrid Approach for Binary Classification of Imbalanced Data—0
0-dimensional Homology Preserving Dimensionality Reduction with TopoMap—0
BDEC:Brain Deep Embedded Clustering model—0
Beam-Space MIMO Radar for Joint Communication and Sensing with OTFS Modulation—0
Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction—0
Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans—0
An Analysis of the t-SNE Algorithm for Data Visualization—0
Applying Ricci Flow to High Dimensional Manifold Learning—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