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 1461–1470 of 3304 papers

TitleStatusHype
Graph Learning via Spectral Densification—0
Graph Neural Network Acceleration via Matrix Dimension Reduction—0
Graphon Pooling for Reducing Dimensionality of Signals and Convolutional Operators on Graphs—0
Graphon Pooling in Graph Neural Networks—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Graph Regularized NMF with L20-norm for Unsupervised Feature Learning—0
Handling Overlapping Asymmetric Datasets -- A Twice Penalized P-Spline Approach—0
Graph Signal Representation with Wasserstein Barycenters—0
Graph Transformer-Based Flood Susceptibility Mapping: Application to the French Riviera and Railway Infrastructure Under Climate Change—0
Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products—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