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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 951–975 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
Image Reconstruction via Deep Image Prior SubspacesCode0
DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning ModelsCode0
Novel Epileptic Seizure Detection Techniques and their Empirical Analysis—0
PAC-Bayesian Generalization Bounds for Adversarial Generative ModelsCode0
Linearized Wasserstein dimensionality reduction with approximation guarantees—0
Kernelized Diffusion mapsCode1
Sparse Dimensionality Reduction Revisited—0
Low-dimensional Data-based Surrogate Model of a Continuum-mechanical Musculoskeletal System Based on Non-intrusive Model Order Reduction—0
Mixed Multi-Model Semantic Interaction for Graph-based Narrative Visualizations—0
Dimension Reduction and MARSCode0
Dimension reduction and redundancy removal through successive Schmidt decompositions—0
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning ConventionsCode0
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems—0
Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps—0
LiteVR: Interpretable and Lightweight Cybersickness Detection using Explainable AI—0
Learning Topology-Preserving Data RepresentationsCode1
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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