SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 751–760 of 3304 papers

TitleStatusHype
Randomized Principal Component Analysis for Hyperspectral Image Classification—0
Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy—0
Efficiently Computing Similarities to Private Datasets—0
A Multimodal Intermediate Fusion Network with Manifold Learning for Stress Detection—0
Supervised Time Series Classification for Anomaly Detection in Subsea Engineering—0
Signed graphs in data sciences via communicability geometry—0
Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)—0
DiffRed: Dimensionality Reduction guided by stable rankCode0
tLaSDI: Thermodynamics-informed latent space dynamics identification—0
An Adaptive Dimension Reduction Estimation Method for High-dimensional Bayesian Optimization—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