SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 2901–2910 of 3304 papers

TitleStatusHype
Random projections of random manifolds—0
Fitting a Simplicial Complex using a Variation of k-means—0
Incomplete Pivoted QR-based Dimensionality Reduction—0
Convergence rates of Kernel Conjugate Gradient for random design regression—0
Improving Sparse Representation-Based Classification Using Local Principal Component Analysis—0
Text comparison using word vector representations and dimensionality reduction—0
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series—0
Manifold Approximation by Moving Least-Squares Projection (MMLS)—0
Continuum directions for supervised dimension reduction—0
An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels—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