SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 901–910 of 3304 papers

TitleStatusHype
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets—0
Dimensionality Reduction for Categorical Data—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—0
Dimensionality Reduction for Data in Multiple Feature Representations—0
Dimensionality Reduction for General KDE Mode Finding—0
A Multi-Fidelity Methodology for Reduced Order Models with High-Dimensional Inputs—0
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation—0
Dimensionality Reduction for k-means Clustering—0
Classic machine learning methods—0
An Impossibility Theorem for Node Embedding—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