SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1871–1880 of 3304 papers

TitleStatusHype
Randomized Principal Component Analysis for Hyperspectral Image Classification—0
Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold—0
Randomized Sketches of Convex Programs with Sharp Guarantees—0
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction—0
Random Manhattan Integer Indexing: Incremental L1 Normed Vector Space Construction—0
Random matrix approach to estimation of high-dimensional factor models—0
Random Maxout Features—0
Random Positive-Only Projections: PPMI-Enabled Incremental Semantic Space Construction—0
Random Projection Estimation of Discrete-Choice Models with Large Choice Sets—0
Random Projections for Improved Adversarial Robustness—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