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 1401–1425 of 3304 papers

TitleStatusHype
Consistent Estimation of Low-Dimensional Latent Structure in High-Dimensional Data—0
Agriculture Commodity Arrival Prediction using Remote Sensing Data: Insights and Beyond—0
Cone-Constrained Principal Component Analysis—0
Generalized Principal Component Analysis—0
Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain—0
Supervised tensor decomposition with features on multiple modes—0
Free Lunch for Gait Recognition: A Novel Relation Descriptor—0
Generative adversarial learning with optimal input dimension and its adaptive generator architecture—0
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey—0
Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification—0
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information—0
Fréchet Cumulative Covariance Net for Deep Nonlinear Sufficient Dimension Reduction with Random Objects—0
FRE: A Fast Method For Anomaly Detection And Segmentation—0
Generative Modeling: A Review—0
Generative Modeling for Atmospheric Convection—0
Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
GENHOP: An Image Generation Method Based on Successive Subspace Learning—0
Fractal Descriptors of Texture Images Based on the Triangular Prism Dimension—0
Genomics Data Analysis via Spectral Shape and Topology—0
GenURL: A General Framework for Unsupervised Representation Learning—0
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds—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
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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