SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 451–460 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
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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