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 776–800 of 3304 papers

TitleStatusHype
A Robust and Efficient Boundary Point Detection Method by Measuring Local Direction Dispersion—0
Deep Features for training Support Vector Machine—0
A Robust Approach for Securing Audio Classification Against Adversarial Attacks—0
Deep Gaussian Mixture Models—0
Artificial Intelligence and Dimensionality Reduction: Tools for approaching future communications—0
A selective review of sufficient dimension reduction for multivariate response regression—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
A Fully Convolutional Network for MR Fingerprinting—0
A Semiparametric Approach to Interpretable Machine Learning—0
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images—0
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods—0
Estimation of a function of low local dimensionality by deep neural networks—0
Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures—0
Deep Learning Architecture for Motor Imaged Words—0
Deep-learning based measurement of planetary radial velocities in the presence of stellar variability—0
Deep Learning for Causal Inference—0
Deep Learning for Efficient GWAS Feature Selection—0
Detection and Evaluation of Clusters within Sequential Data—0
Click prediction boosting via Bayesian hyperparameter optimization based ensemble learning pipelines—0
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—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
An Incremental Non-Linear Manifold Approximation Method—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