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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 1026–1050 of 3304 papers

TitleStatusHype
A Deep Graph Embedding Network Model for Face Recognition—0
Dimensionality Reduction for Categorical Data—0
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets—0
Dimensionality reduction for acoustic vehicle classification with spectral embedding—0
Likelihood Contribution based Multi-scale Architecture for Generative Flows—0
Autoencoder Enhanced Realised GARCH on Volatility Forecasting—0
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning—0
Dimensionality Reduction Ensembles—0
Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification—0
Dimensionality reduction based on Distance Preservation to Local Mean (DPLM) for SPD matrices and its application in BCI—0
Dimensionality Reduction as Probabilistic Inference—0
Auto-Detection of Safety Issues in Baby Products—0
A Deep Embedded Refined Clustering Approach for Breast Cancer Distinction based on DNA Methylation—0
Achieving Fair PCA Using Joint Eigenvalue Decomposition—0
3-Dimensional residual neural architecture search for ultrasonic defect detection—0
Automated Classification of Dry Bean Varieties Using XGBoost and SVM Models—0
Visualization of AE's Training on Credit Card Transactions with Persistent Homology—0
Dimensionality Reduction and Wasserstein Stability for Kernel Regression—0
Dimensionality Reduction and State Space Systems: Forecasting the US Treasury Yields Using Frequentist and Bayesian VARs—0
Auto-adaptative Laplacian Pyramids for High-dimensional Data Analysis—0
Dimensionality Reduction and Prioritized Exploration for Policy Search—0
Dimensionality Reduction and Motion Clustering during Activities of Daily Living: 3, 4, and 7 Degree-of-Freedom Arm Movements—0
A Unifying Family of Data-Adaptive Partitioning Algorithms—0
Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images—0
Dimensionality reduction, and function approximation of poly(lactic-co-glycolic acid) micro- and nanoparticle dissolution rate—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