SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 13811390 of 3304 papers

TitleStatusHype
A dynamical systems based framework for dimension reduction0
Wassmap: Wasserstein Isometric Mapping for Image Manifold LearningCode0
Assessment of convolutional recurrent autoencoder network for learning wave propagation0
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification0
T- Hop: Tensor representation of paths in graph convolutional networks0
Weight Matrix Dimensionality Reduction in Deep Learning via Kronecker Multi-layer ArchitecturesCode0
MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems0
Covariance matrix preparation for quantum principal component analysis0
Knowledge Base Index Compression via Dimensionality and Precision ReductionCode0
An efficient real-time target tracking algorithm using adaptive feature fusion0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9Unverified
2tSNEClassification Accuracy51.5Unverified
3IVISClassification Accuracy46.6Unverified
4UMAPClassification Accuracy41.3Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1Unverified
2QSClassification Accuracy68Unverified