SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2471–2480 of 3304 papers

TitleStatusHype
Noncommutative Model Selection for Data Clustering and Dimension Reduction Using Relative von Neumann Entropy—0
Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals—0
Non-Euclidean Self-Organizing Maps—0
Non-Gaussian Component Analysis using Entropy Methods—0
Non-intrusive surrogate modelling using sparse random features with applications in crashworthiness analysis—0
Non-linear dimensionality reduction: Riemannian metric estimation and the problem of geometric discovery—0
Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets—0
Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-based Approach—0
Nonlinear Dimensionality Reduction on Graphs—0
Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era—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