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 2871–2880 of 3304 papers

TitleStatusHype
Large Scale Behavioral Analytics via Topical Interaction—0
Interacting with Massive Behavioral Data—0
Comparison among dimensionality reduction techniques based on Random Projection for cancer classification—0
Unsupervised Feature Selection Based on the Morisita Estimator of Intrinsic Dimension—0
Probabilistic Data Analysis with Probabilistic ProgrammingCode0
Training Echo State Networks with Regularization through Dimensionality Reduction—0
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data—0
Robust High-Dimensional Linear Regression—0
Bayesian Learning of Dynamic Multilayer Networks—0
Quantum Algorithms for Compositional Natural Language ProcessingCode1
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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