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 1891–1900 of 3304 papers

TitleStatusHype
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity—0
Gaussian Process Latent Variable Flows for Massively Missing Data—0
Gaze-Sensing LEDs for Head Mounted Displays—0
Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach—0
Bayesian Inference over the Stiefel Manifold via the Givens Representation—0
Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Generalizable Spectral Embedding with an Application to UMAP—0
Generalization Ability Analysis of Through-the-Wall Radar Human Activity Recognition—0
Generalized BackPropagation, Étude De Cas: Orthogonality—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