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 2401–2425 of 3304 papers

TitleStatusHype
A Low Effort Approach to Structured CNN Design Using PCA—0
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach—0
Class Mean Vector Component and Discriminant Analysis—0
Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach—0
A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)—0
Graph Signal Representation with Wasserstein Barycenters—0
Classification of Cervical Cancer Dataset—0
Multi-Dimensional Scaling on Groups—0
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder ApproachCode0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks—0
Time Series Featurization via Topological Data Analysis—0
SqueezeFit: Label-aware dimensionality reduction by semidefinite programmingCode0
Machine Learning of coarse-grained Molecular Dynamics Force Fields—0
Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach—0
GAN-EM: GAN based EM learning framework—0
Robust Subspace Approximation in a Stream—0
Model-based targeted dimensionality reduction for neuronal population data—0
Manifold Coordinates with Physical MeaningCode0
RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology—0
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration—0
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study—0
Enhanced Expressive Power and Fast Training of Neural Networks by Random ProjectionsCode0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
Global Sensitivity Analysis of High Dimensional Neuroscience Models: An Example of Neurovascular Coupling—0
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images—0
Show:102550
← PrevPage 97 of 133Next →
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