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Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–475 of 3304 papers

TitleStatusHype
Neural Networks Perform Sufficient Dimension ReductionCode0
Generative Modeling: A Review—0
Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data—0
Bi-Sparse Unsupervised Feature SelectionCode0
A Unifying Family of Data-Adaptive Partitioning Algorithms—0
Fast Multi-Group Gaussian Process Factor Models—0
Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis—0
Computing Gram Matrix for SMILES Strings using RDKFingerprint and Sinkhorn-Knopp Algorithm—0
ICA-based Resting-State Networks Obtained on Large Autism fMRI Dataset ABIDECode0
Progressive Monitoring of Generative Model Training Evolution—0
Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian OptimizationCode0
CSI Compression using Channel Charting—0
Representation learning of dynamic networks—0
Alternative Channel Charting Techniques in Cellular Wireless Communications—0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction—0
Agtech Framework for Cranberry-Ripening Analysis Using Vision Foundation Models—0
When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities—0
A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition—0
Nested Diffusion Models Using Hierarchical Latent Priors—0
A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing—0
Deep Learning, Machine Learning, Advancing Big Data Analytics and Management—0
An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction—0
Traversing the Subspace of Adversarial Patches—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