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 2661–2670 of 3304 papers

TitleStatusHype
Bidirectional deep-readout echo state networksCode0
Dimensionality Reduction on Grassmannian via Riemannian Optimization: A Generalized Perspective—0
A New Method for Performance Analysis in Nonlinear Dimensionality Reduction—0
Sparse quadratic classification rules via linear dimension reductionCode0
Evaluating deep variational autoencoders trained on pan-cancer gene expression—0
Hand Gesture Recognition with Leap Motion—0
Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM—0
Dimension Estimation Using Random Connection Models—0
Bayesian model and dimension reduction for uncertainty propagation: applications in random media—0
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA—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