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 1501–1510 of 3304 papers

TitleStatusHype
Three-body renormalization group limit cycles based on unsupervised feature learning—0
ELBD: Efficient score algorithm for feature selection on latent variables of VAE—0
Auto-NAHL: A Neural Network Approach for Condition-Based Maintenance of Complex Industrial SystemsCode0
A Data Quarantine Model to Secure Data in Edge Computing—0
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method—0
Leveraging Unsupervised Image Registration for Discovery of Landmark Shape DescriptorCode0
Efficient Binary Embedding of Categorical Data using BinSketch—0
Active Linear Regression for _p Norms and Beyond—0
High Performance Out-of-sample Embedding Techniques for Multidimensional Scaling—0
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval—0
Show:102550
← PrevPage 151 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