SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 31–40 of 3304 papers

TitleStatusHype
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximationsCode1
Bayesian Optimization of Sampling Densities in MRICode1
CatBoost: gradient boosting with categorical features supportCode1
Effective Sample Size, Dimensionality, and Generalization in Covariate Shift AdaptationCode1
ATD: Augmenting CP Tensor Decomposition by Self SupervisionCode1
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breathCode1
Algorithmic Stability and Generalization of an Unsupervised Feature Selection AlgorithmCode1
On Path Integration of Grid Cells: Group Representation and Isotropic ScalingCode1
A Spectral Method for Assessing and Combining Multiple Data VisualizationsCode1
A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiologyCode1
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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