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 2801–2825 of 3304 papers

TitleStatusHype
Auto-adaptative Laplacian Pyramids for High-dimensional Data Analysis—0
Auto-Detection of Safety Issues in Baby Products—0
Autoencoder Enhanced Realised GARCH on Volatility Forecasting—0
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets—0
Autoencoding topology—0
Autoencoding with a Learning Classifier System: Initial Results—0
Automated Anomaly Detection on European XFEL Klystrons—0
Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis—0
Automated Disease Normalization with Low Rank Approximations—0
Automatic Collection Creation and Recommendation—0
Automatic dimensionality reduction of Twin-in-the-Loop Observers—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
Automatic Prediction of the Performance of Every Parser—0
Automatic Selection of t-SNE Perplexity—0
Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response—0
Autonomous Dimension Reduction by Flattening Deformation of Data Manifold under an Intrinsic Deforming Field—0
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents—0
AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images—0
Auto-weighted Mutli-view Sparse Reconstructive Embedding—0
A VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction—0
AVIDA: Alternating method for Visualizing and Integrating Data—0
A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction—0
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration—0
A Wasserstein perspective of Vanilla GANs—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