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 691–700 of 3304 papers

TitleStatusHype
Application of Fuzzy Clustering for Text Data Dimensionality Reduction—0
Convergence rates of Kernel Conjugate Gradient for random design regression—0
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs—0
Application of Symmetric Uncertainty and Mutual Information to Dimensionality Reduction and Classification of Hyperspectral Images—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
A Fully Convolutional Network for MR Fingerprinting—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