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 1151–1160 of 3304 papers

TitleStatusHype
A Low Effort Approach to Structured CNN Design Using PCA—0
A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images—0
Deep Sufficient Representation Learning via Mutual Information—0
A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection—0
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks—0
DeepRT: deep learning for peptide retention time prediction in proteomics—0
Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective—0
The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study—0
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets—0
A study of the classification of low-dimensional data with supervised manifold learning—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