SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 3211–3220 of 3304 papers

TitleStatusHype
A Novel Approach for Single Gene Selection Using Clustering and Dimensionality Reduction—0
Spectral Convergence of the connection Laplacian from random samples—0
Diffusion map for clustering fMRI spatial maps extracted by independent component analysis—0
Improving Lexical Semantics for Sentential Semantics: Modeling Selectional Preference and Similar Words in a Latent Variable Model—0
Analytic Bilinear Appearance Subspace Construction for Modeling Image Irradiance under Natural Illumination and Non-Lambertian Reflectance—0
Learning Multiple Non-linear Sub-spaces Using K-RBMs—0
Histograms of Sparse Codes for Object Detection—0
Sparse Subspace Denoising for Image Manifolds—0
Local Fisher Discriminant Analysis for Pedestrian Re-identification—0
Non-linear dimensionality reduction: Riemannian metric estimation and the problem of geometric discovery—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