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Dimensionality Reduction

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

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Papers

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Showing 1801–1825 of 3304 papers

TitleStatusHype
Predicting Molecular Phenotypes with Single Cell RNA Sequencing Data: an Assessment of Unsupervised Machine Learning Models—0
Prediction of Auto Insurance Risk Based on t-SNE Dimensionality Reduction—0
Prediction of daily maximum ozone levels using Lasso sparse modeling method—0
Prediction with Dimension Reduction of Multiple Molecular Data Sources for Patient Survival—0
Predictive Liability Models and Visualizations of High Dimensional Retail Employee Data—0
Preliminary Steps Towards Federated Sentiment Classification—0
Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic Optimization—0
Preserving clusters and correlations: a dimensionality reduction method for exceptionally high global structure preservation—0
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy—0
PRHLT: Combination of Deep Autoencoders with Classification and Regression Techniques for SemEval-2015 Task 11—0
Primary Care Diagnoses as a Reliable Predictor for Orthopedic Surgical Interventions—0
Principal Component Analysis When n < p: Challenges and Solutions—0
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering—0
Principal Geodesic Analysis of Merge Trees (and Persistence Diagrams)—0
Principal Model Analysis Based on Partial Least Squares—0
Principal Polynomial Analysis—0
Principal subbundles for dimension reduction—0
Principal Component Analysis based frameworks for efficient missing data imputation algorithms—0
Privacy-Enhancing Context Authentication from Location-Sensitive Data—0
FedPower: Privacy-Preserving Distributed Eigenspace Estimation—0
Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data—0
Privacy-Preserving Machine Learning Using EtC Images—0
PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks—0
Probabilistic Bearing Fault Diagnosis Using Gaussian Process with Tailored Feature Extraction—0
Probabilistic Dimensionality Reduction via Structure 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