SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 121–130 of 3304 papers

TitleStatusHype
A local approach to parameter space reduction for regression and classification tasksCode1
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate modelsCode1
Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid SimulationsCode1
Generative locally linear embedding: A module for manifold unfolding and visualizationCode1
Deep Learning for Functional Data Analysis with Adaptive Basis LayersCode1
SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate CurvatureCode1
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICACode1
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in PythonCode1
ATD: Augmenting CP Tensor Decomposition by Self SupervisionCode1
Improving Metric Dimensionality Reduction with Distributed TopologyCode1
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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