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 1841–1850 of 3304 papers

TitleStatusHype
Finding Rule-Interpretable Non-Negative Data Representation—0
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—0
Firing Rate Dynamics in Recurrent Spiking Neural Networks with Intrinsic and Network Heterogeneity—0
Firm Heterogeneity and Macroeconomic Fluctuations: a Functional VAR model—0
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces—0
First-order bifurcation detection for dynamic complex networks—0
Fitting a Simplicial Complex using a Variation of k-means—0
Flashlight Search Medial Axis: A Pixel-Free Pore-Network Extraction Algorithm—0
Flexible sampling of discrete data correlations without the marginal distributions—0
Forecastable Component Analysis (ForeCA)—0
Show:102550
← PrevPage 185 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