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 1991–2000 of 3304 papers

TitleStatusHype
Geomstats: A Python Package for Riemannian Geometry in Machine LearningCode2
Backprojection for Training Feedforward Neural Networks in the Input and Feature SpacesCode0
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese NetworksCode0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—0
Weighted Fisher Discriminant Analysis in the Input and Feature SpacesCode0
Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders—0
DNA Methylation Data to Predict Suicidal and Non-Suicidal Deaths: A Machine Learning Approach—0
Company classification using machine learning—0
Flows for simultaneous manifold learning and density estimationCode1
A new set of cluster driven composite development indicators—0
Show:102550
← PrevPage 200 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