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 371–380 of 3304 papers

TitleStatusHype
Inteval Analysis for two spherical functions arising from robust Perspective-n-Lines problem—0
Hidden Convexity of Fair PCA and Fast Solver via Eigenvalue OptimizationCode0
Analysis of Evolving Cortical Neuronal Networks Using Visual Informatics—0
Tuning-Free Structured Sparse PCA via Deep Unfolding NetworksCode0
Beyond Worst-Case Dimensionality Reduction for Sparse Vectors—0
Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality ReductionCode0
RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering—0
Forward-Cooperation-Backward (FCB) learning in a Multi-Encoding Uni-Decoding neural network architecture—0
BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractor—0
Empirical likelihood approach for high-dimensional moment restrictions with dependent dataCode0
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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