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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 226–250 of 3304 papers

TitleStatusHype
A Graphical Approach to State Variable Selection in Off-policy Learning—0
Unified dimensionality reduction techniques in chronic liver disease detection—0
Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing dataCode1
Revisiting PCA for time series reduction in temporal dimensionCode7
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
Fast Multi-Group Gaussian Process Factor Models—0
A Unifying Family of Data-Adaptive Partitioning Algorithms—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
Dimension Reduction with Locally Adjusted GraphsCode4
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
CSI Compression using Channel Charting—0
Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian OptimizationCode0
Representation learning of dynamic networks—0
Alternative Channel Charting Techniques in Cellular Wireless Communications—0
Agtech Framework for Cranberry-Ripening Analysis Using Vision Foundation Models—0
Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction—0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—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
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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