SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2131–2140 of 3304 papers

TitleStatusHype
Iterative Cluster Harvesting for Wafer Map Defect Patterns—0
IT-map: an Effective Nonlinear Dimensionality Reduction Method for Interactive Clustering—0
Statistical feature embedding for heart sound classification—0
Jaeger: A Concatenation-Based Multi-Transformer VQA Model—0
Johnson-Lindenstrauss embeddings for noisy vectors -- taking advantage of the noise—0
Johnson-Lindenstrauss Lemma, Linear and Nonlinear Random Projections, Random Fourier Features, and Random Kitchen Sinks: Tutorial and Survey—0
Unraveling the Veil of Subspace RIP Through Near-Isometry on Subspaces—0
Joint Characterization of Multiscale Information in High Dimensional Data—0
Joint Characterization of Spatiotemporal Data Manifolds—0
Joint Characterization of the Cryospheric Spectral Feature Space—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