SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2141–2150 of 3304 papers

TitleStatusHype
Joint Dimensionality Reduction and Metric Learning: A Geometric Take—0
Joint Dimensionality Reduction for Separable Embedding Estimation—0
Joint Dimensionality Reduction for Two Feature Vectors—0
Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling—0
Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints—0
Joint Semi-supervised RSS Dimensionality Reduction and Fingerprint Based Algorithm for Indoor Localization—0
Kernel methods for detecting coherent structures in dynamical data—0
Kernelized Multiview Subspace Analysis by Self-weighted Learning—0
Kernel Methods for the Approximation of Nonlinear Systems—0
Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods—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