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 1871–1880 of 3304 papers

TitleStatusHype
Full-dimensional characterisation of time-warped spike-time stimulus-response distribution geometries—0
Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data—0
Functional Inverse Regression in an Enlarged Dimension Reduction Space—0
Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data—0
Functional sufficient dimension reduction through information maximization with application to classification—0
Functorial Manifold Learning—0
Fusing Vector Space Models for Domain-Specific Applications—0
Fusion of heterogeneous bands and kernels in hyperspectral image processing—0
Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery—0
Fuzzy Pooling—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