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 951–960 of 3304 papers

TitleStatusHype
CAMEL: Curvature-Augmented Manifold Embedding and Learning—0
Comparative Studies of Unsupervised and Supervised Learning Methods based on Multimedia Applications—0
Graph-based Extreme Feature Selection for Multi-class Classification Tasks—0
Large Deviations for Accelerating Neural Networks Training—0
In search of the most efficient and memory-saving visualization of high dimensional data—0
Wasserstein Projection Pursuit of Non-Gaussian Signals—0
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive FilteringCode0
Deep Kernel Principal Component Analysis for Multi-level Feature LearningCode0
nSimplex Zen: A Novel Dimensionality Reduction for Euclidean and Hilbert SpacesCode0
DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning ModelsCode0
Show:102550
← PrevPage 96 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