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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1001–1025 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
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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