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 201–210 of 3304 papers

TitleStatusHype
Foundations of a Knee Joint Digital Twin from qMRI Biomarkers for Osteoarthritis and Knee Replacement—0
A dimensionality reduction technique based on the Gromov-Wasserstein distance—0
Low-Dimensional Representation-Driven TSK Fuzzy System for Feature Selection—0
Hybrid Two-Stage Reconstruction of Multiscale Subsurface Flow with Physics-informed Residual Connected Neural Operator—0
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach—0
Ultralow-dimensionality reduction for identifying critical transitions by spatial-temporal PCA—0
Using Space-Filling Curves and Fractals to Reveal Spatial and Temporal Patterns in Neuroimaging DataCode0
Lee and Seung (2000)'s Algorithms for Non-negative Matrix Factorization: A Supplementary Proof Guide—0
On the dimension of pullback attractors in recurrent neural networks—0
Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning ApproachCode0
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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