SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1091–1100 of 3304 papers

TitleStatusHype
Identifying Selections Operating on HIV-1 Reverse Transcriptase via Uniform Manifold Approximation and ProjectionCode0
On The Relative Error of Random Fourier Features for Preserving Kernel Distance—0
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental AnalysisCode0
A canonical correlation-based framework for performance analysis of radio access networks—0
Patients' Severity States Classification based on Electronic Health Record (EHR) Data using Multiple Machine Learning and Deep Learning ApproachesCode0
Spectral Diffusion Processes—0
Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach—0
Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature DescriptorsCode1
Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)—0
On Projections to Linear SubspacesCode0
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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