SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 2411–2420 of 3304 papers

TitleStatusHype
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images—0
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft—0
Subspace Clustering through Sub-ClustersCode0
Exploring the Deep Feature Space of a Cell Classification Neural Network—0
Unsupervised learning with contrastive latent variable modelsCode0
Interactive dimensionality reduction using similarity projections—0
Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders—0
Matrix Product Operator Restricted Boltzmann Machines—0
Semi-supervised Deep Representation Learning for Multi-View Problems—0
Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with 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