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 1761–1770 of 3304 papers

TitleStatusHype
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty—0
Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off—0
PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation—0
Penalized Principal Component Regression on Graphs for Analysis of Subnetworks—0
People Tracking with the Laplacian Eigenmaps Latent Variable Model—0
Perceptual Visual Interactive Learning—0
Perfecting Liquid-State Theories with Machine Intelligence—0
Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers—0
Performance Evaluation of t-SNE and MDS Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers—0
Performance Examination of Symbolic Aggregate Approximation in IoT Applications—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