SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1251–1260 of 3304 papers

TitleStatusHype
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval—0
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
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
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental AnalysisCode0
Spectral Diffusion Processes—0
Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach—0
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