SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1171–1180 of 3304 papers

TitleStatusHype
KIDS: kinematics-based (in)activity detection and segmentation in a sleep case study—0
ClusTop: An unsupervised and integrated text clustering and topic extraction framework—0
Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction—0
A Functional approach for Two Way Dimension Reduction in Time Series—0
Causal Deep Learning—0
Quantum Machine Learning Applied to the Classification of Diabetes—0
Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis—0
Theoretical Guarantees for Sparse Principal Component Analysis based on the Elastic Net—0
Topological Data Analysis of Spatial Patterning in Heterogeneous Cell Populations: Clustering and Sorting with Varying Cell-Cell Adhesion—0
ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight ConditionsCode0
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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