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 3051–3075 of 3304 papers

TitleStatusHype
Deep Learning for Efficient GWAS Feature Selection—0
Deep Learning for GWP Prediction: A Framework Using PCA, Quantile Transformation, and Ensemble Modeling—0
Deep Learning for Size and Microscope Feature Extraction and Classification in Oral Cancer: Enhanced Convolution Neural Network—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Deep Learning, Machine Learning, Advancing Big Data Analytics and Management—0
Deep Learning Multidimensional Projections—0
Deep Linear Discriminant Analysis with Variation for Polycystic Ovary Syndrome Classification—0
Deep Manifold Computing and Visualization Using Elastic Locally Isometric Smoothness—0
Deep Manifold Transformation for Nonlinear Dimensionality Reduction—0
Deep matrix factorizations—0
Deep Monocular Visual Odometry for Ground Vehicle—0
Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows—0
Deep neural networks for the evaluation and design of photonic devices—0
Generalizing Correspondence Analysis for Applications in Machine Learning—0
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets—0
Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective—0
DeepRT: deep learning for peptide retention time prediction in proteomics—0
Deep Sufficient Representation Learning via Mutual Information—0
Deep topic modeling by multilayer bootstrap network and lasso—0
Deep Triphone Embedding Improves Phoneme Recognition—0
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses—0
Deep Variational Sufficient Dimensionality Reduction—0
Defining Reference Sequences for Nocardia Species by Similarity and Clustering Analyses of 16S rRNA Gene Sequence Data—0
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations—0
DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects—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