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 1451–1475 of 3304 papers

TitleStatusHype
Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension—0
Grammar-based Ordinary Differential Equation Discovery—0
Granger Causality Based Hierarchical Time Series Clustering for State Estimation—0
Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation—0
ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data—0
A Correspondence Analysis Framework for Author-Conference Recommendations—0
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft—0
FlowBERT: Prompt-tuned BERT for variable flow field prediction—0
Fourier-Invertible Neural Encoder (FINE) for Homogeneous Flows—0
Fourier analysis perspective for sufficient dimension reduction problem—0
Graph Learning via Spectral Densification—0
Graph Neural Network Acceleration via Matrix Dimension Reduction—0
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization—0
Graphon Pooling in Graph Neural Networks—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Graph Regularized NMF with L20-norm for Unsupervised Feature Learning—0
Foundations of Coupled Nonlinear Dimensionality Reduction—0
Graph Signal Representation with Wasserstein Barycenters—0
Graph Transformer-Based Flood Susceptibility Mapping: Application to the French Riviera and Railway Infrastructure Under Climate Change—0
Foundations of a Knee Joint Digital Twin from qMRI Biomarkers for Osteoarthritis and Knee Replacement—0
Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach—0
Forward-Cooperation-Backward (FCB) learning in a Multi-Encoding Uni-Decoding neural network architecture—0
Grassmann Averages for Scalable Robust PCA—0
Grassmann Graph Embedding—0
Formation-Controlled Dimensionality Reduction—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