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 881–890 of 3304 papers

TitleStatusHype
A Dimensionality Reduction Approach for Convolutional Neural Networks—0
Bayesian Compressed Regression—0
Analysing race and sex bias in brain age prediction—0
Bayesian Calibration for Activity Based Models—0
Analyse en Composante Principale pour l'extraction des i-vecteurs en v\'erification du locuteur (Principal Component Analysis for i-vector extraction in speaker verification.) [in French]—0
A determinantal point process for column subset selection—0
A witness function based construction of discriminative models using Hermite polynomials—0
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems—0
A description length approach to determining the number of k-means clusters—0
A clustering adaptive Gaussian process regression method: response patterns based real-time prediction for nonlinear solid mechanics problems—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