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 2111–2120 of 3304 papers

TitleStatusHype
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold—0
Interpretable Linear Dimensionality Reduction based on Bias-Variance Analysis—0
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
Interpretable Semantic Vectors from a Joint Model of Brain- and Text- Based Meaning—0
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge—0
Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs—0
Inteval Analysis for two spherical functions arising from robust Perspective-n-Lines problem—0
Intrinsic Grassmann Averages for Online Linear, Robust and Nonlinear Subspace Learning—0
Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors—0
Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network—0
Show:102550
← PrevPage 212 of 331Next →
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