SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 1061–1070 of 3304 papers

TitleStatusHype
Functional sufficient dimension reduction through information maximization with application to classification—0
Learning low-dimensional dynamics from whole-brain data improves task capture—0
State Representation Learning Using an Unbalanced AtlasCode0
Intrinsic statistical separation of subpopulations in heterogeneous collective motion via dimensionality reductionCode0
Spectral Clustering via Orthogonalization-Free MethodsCode0
A Note on Dimensionality Reduction in Deep Neural Networks using Empirical Interpolation Method—0
Small-data Reduced Order Modeling of Chaotic Dynamics through SyCo-AE: Synthetically Constrained Autoencoders—0
Agile gesture recognition for capacitive sensing devices: adapting on-the-job—0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—0
Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reduction—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