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 871–880 of 3304 papers

TitleStatusHype
Analysis of Evolving Cortical Neuronal Networks Using Visual Informatics—0
Bayesian inverse regression for dimension reduction with small datasets—0
Bayesian Inverse Problems with Conditional Sinkhorn Generative Adversarial Networks in Least Volume Latent Spaces—0
Analysis of Cellular Feature Differences of Astrocytomas with Distinct Mutational Profiles Using Digitized Histopathology Images—0
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction—0
Bayesian full waveform inversion with sequential surrogate model refinement—0
Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization—0
Bayesian Exponential Family PCA—0
Bayesian Data Sketching for Varying Coefficient Regression Models—0
Analysis and mining of low-carbon and energy-saving tourism data characteristics based on machine learning algorithm—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