SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2911–2920 of 3304 papers

TitleStatusHype
A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic—0
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction—0
Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results—0
Discovery of Latent Factors in High-dimensional Data Using Tensor Methods—0
Sifting Common Information from Many VariablesCode0
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets—0
Property-driven State-Space Coarsening for Continuous Time Markov Chains—0
Sequential Principal Curves Analysis—0
Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis—0
Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations—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