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 1321–1330 of 3304 papers

TitleStatusHype
Cooperative Thresholded Lasso for Sparse Linear Bandit—0
Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics—0
Assessment of convolutional recurrent autoencoder network for learning wave propagation—0
Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial—0
Applications of machine learning to predict seasonal precipitation for East Africa—0
Convolutional Autoencoders for Reduced-Order Modeling—0
Convex Optimization Learning of Faithful Euclidean Distance Representations in Nonlinear Dimensionality Reduction—0
Application Research On Real-Time Perception Of Device Performance Status—0
A Harmony Search Based Wrapper Feature Selection Method for Holistic Bangla word Recognition—0
A Cross Entropy test allows quantitative statistical comparison of t-SNE and UMAP representations—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