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 2911–2920 of 3304 papers

TitleStatusHype
Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines—0
Cloud-based Deep Learning of Big EEG Data for Epileptic Seizure Prediction—0
Clustering based on Mixtures of Sparse Gaussian Processes—0
Clustering, Classification, Discriminant Analysis, and Dimension Reduction via Generalized Hyperbolic Mixtures—0
Clustering high dimensional meteorological scenarios: results and performance index—0
Distance preserving model order reduction of graph-Laplacians and cluster analysis—0
Clustering small datasets in high-dimension by random projection—0
Cluster Weighted Model Based on TSNE algorithm for High-Dimensional Data—0
ClusTop: An unsupervised and integrated text clustering and topic extraction framework—0
C(NN)FD -- Deep Learning Modelling of Multi-Stage Axial Compressors Aerodynamics—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