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 2131–2140 of 3304 papers

TitleStatusHype
Capacity Preserving Mapping for High-dimensional Data VisualizationCode0
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings—0
Siamese Neural Networks for Wireless Positioning and Channel Charting—0
A New Covariance Estimator for Sufficient Dimension Reduction in High-Dimensional and Undersized Sample Problems—0
Netboost: Boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease—0
Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding—0
Estimating covariance and precision matrices along subspaces—0
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario—0
Function Preserving Projection for Scalable Exploration of High-Dimensional DataCode0
The space complexity of inner product filters—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