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 381–390 of 3304 papers

TitleStatusHype
Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Approximation Algorithms for Sparse Principal Component Analysis—0
A review of unsupervised learning in astronomy—0
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems—0
A Robust and Efficient Boundary Point Detection Method by Measuring Local Direction Dispersion—0
A Robust Approach for Securing Audio Classification Against Adversarial Attacks—0
Artificial Intelligence and Dimensionality Reduction: Tools for approaching future communications—0
A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images—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