SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2871–2880 of 3304 papers

TitleStatusHype
Interpretable Embeddings From Molecular Simulations Using Gaussian Mixture Variational AutoencodersCode0
A primer on correlation-based dimension reduction methods for multi-omics analysisCode0
Probabilistic Data Analysis with Probabilistic ProgrammingCode0
Interpretable non-linear dimensionality reduction using gaussian weighted linear transformationCode0
Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative ValuesCode0
Ellipsoid fitting with the Cayley transformCode0
Interpretable Visualization and Higher-Order Dimension Reduction for ECoG DataCode0
Self Organizing Nebulous Growths for Robust and Incremental Data VisualizationCode0
A method to integrate and classify normal distributionsCode0
Interpreting LSTM Prediction on Solar Flare Eruption with Time-series ClusteringCode0
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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