SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 2991–3000 of 3304 papers

TitleStatusHype
Class-constrained t-SNE: Combining Data Features and Class ProbabilitiesCode0
Visualizing Representations of Adversarially Perturbed InputsCode0
Characterization of Phosphorylated Tau-Microtubule complex with Molecular Dynamics (MD) simulationCode0
Structured Sparse Non-negative Matrix Factorization with L20-Norm for scRNA-seq Data AnalysisCode0
S+t-SNE -- Bringing Dimensionality Reduction to Data StreamsCode0
Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing DataCode0
Anticancer Peptides Classification using Kernel Sparse Representation ClassifierCode0
Nonlinear Level Set Learning for Function Approximation on Sparse Data with Applications to Parametric Differential EquationsCode0
Unsupervised and Supervised Principal Component Analysis: TutorialCode0
Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized AutoencodersCode0
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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