SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2981–2990 of 3304 papers

TitleStatusHype
Contrastive Multivariate Singular Spectrum Analysis—0
Convergence rates of Kernel Conjugate Gradient for random design regression—0
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs—0
Convex Optimization Learning of Faithful Euclidean Distance Representations in Nonlinear Dimensionality Reduction—0
Convolutional Autoencoders for Reduced-Order Modeling—0
Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial—0
Assessment of convolutional recurrent autoencoder network for learning wave propagation—0
Cooperative Thresholded Lasso for Sparse Linear Bandit—0
Cortical spatio-temporal dimensionality reduction for visual grouping—0
CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data—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