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 1531–1540 of 3304 papers

TitleStatusHype
Computing Approximate _p Sensitivities—0
High Performance Software in Multidimensional Reduction Methods for Image Processing with Application to Ancient Manuscripts—0
A Novel method for Schizophrenia classification using nonlinear features and neural networks—0
A Graph Based Raman Spectral Processing Technique for Exosome Classification—0
First-order bifurcation detection for dynamic complex networks—0
History Matching for Geological Carbon Storage using Data-Space Inversion with Spatio-Temporal Data Parameterization—0
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces—0
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting—0
Firm Heterogeneity and Macroeconomic Fluctuations: a Functional VAR model—0
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—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