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 3081–3090 of 3304 papers

TitleStatusHype
Density-based Isometric Mapping—0
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems—0
Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning—0
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence—0
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study—0
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio—0
Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification—0
Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features—0
Detection and Evaluation of Clusters within Sequential Data—0
Detection and Identification Accuracy of PCA-Accelerated Real-Time Processing of Hyperspectral Imagery—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