SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1721–1730 of 3304 papers

TitleStatusHype
Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features—0
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples—0
SCALE SPACE FLOW WITH AUTOREGRESSIVE PRIORS—0
Lazy FSCA for Unsupervised Variable SelectionCode0
FedPower: Privacy-Preserving Distributed Eigenspace Estimation—0
The Mathematics Behind Spectral Clustering And The Equivalence To PCA—0
Unsupervised dynamic modeling of medical image transformationCode0
Validating Clustering Frameworks for Electric Load Demand ProfilesCode0
t-SNE, Forceful Colorings and Mean Field Limits—0
Quantization Algorithms for Random Fourier Features—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