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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 1001–1050 of 3304 papers

TitleStatusHype
Distance metric learning based on structural neighborhoods for dimensionality reduction and classification performance improvement—0
Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis—0
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features—0
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models—0
Automated Anomaly Detection on European XFEL Klystrons—0
Distributed estimation of principal support vector machines for sufficient dimension reduction—0
A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography—0
Distributed Low-Rank Estimation Based on Joint Iterative Optimization in Wireless Sensor Networks—0
A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs—0
A Class-Based Agreement Model for Generating Accurately Inflected Translations—0
Dimensionality reduction for prediction: Application to Bitcoin and Ethereum—0
Dimensionality reduction for k-means clustering of large-scale influenza mutation datasets—0
Dimensionality Reduction for k-means Clustering—0
Distributionally Robust and Multi-Objective Nonnegative Matrix Factorization—0
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation—0
Autoencoding with a Learning Classifier System: Initial Results—0
A Multimodal Data-driven Framework for Anxiety Screening—0
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein—0
Dimensionality Reduction for General KDE Mode Finding—0
Distributional Semantics in R with the wordspace Package—0
Distribution-based Label Space Transformation for Multi-label Learning—0
Dimensionality Reduction for Data in Multiple Feature Representations—0
Dimensionality reduction for click-through rate prediction: Dense versus sparse representation—0
Autoencoding topology—0
A Multi-Fidelity Methodology for Reduced Order Models with High-Dimensional Inputs—0
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