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 1001–1010 of 3304 papers

TitleStatusHype
Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans—0
Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural NetworksCode0
On Sufficient Graphical Models—0
Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data—0
Bayesian tomography using polynomial chaos expansion and deep generative networks—0
Differential Privacy for Clustering Under Continual Observation—0
Principal subbundles for dimension reduction—0
ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value RegularizationCode0
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions—0
Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams)—0
Show:102550
← PrevPage 101 of 331Next →
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