SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2171–2180 of 3304 papers

TitleStatusHype
Laplacian Matrix for Dimensionality Reduction and Clustering—0
Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs—0
Large data limits and scaling laws for tSNE—0
Large Deviations for Accelerating Neural Networks Training—0
Large Margin Discriminant Dimensionality Reduction in Prediction Space—0
Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI)—0
Large Scale Behavioral Analytics via Topical Interaction—0
Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI—0
Large Scale Passenger Detection with Smartphone/Bus Implicit Interaction and Multisensory Unsupervised Cause-effect Learning—0
Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning—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