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 2001–2010 of 3304 papers

TitleStatusHype
Segmenting thalamic nuclei from manifold projections of multi-contrast MRI—0
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction—0
Self-calibrating Neural Networks for Dimensionality Reduction—0
Self-Expressive Decompositions for Matrix Approximation and Clustering—0
Self-paced Principal Component Analysis—0
Self-Paced Probabilistic Principal Component Analysis for Data with Outliers—0
Self-Supervised Graph Embedding Clustering—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Self-Supervised Training with Autoencoders for Visual Anomaly Detection—0
Semantic-Preserving Feature Partitioning for Multi-View Ensemble 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