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 1701–1725 of 3304 papers

TitleStatusHype
On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiologic boundary conditions—0
On the Power of SVD in the Stochastic Block Model—0
On the reduction of Linear Parameter-Varying State-Space models—0
On The Relative Error of Random Fourier Features for Preserving Kernel Distance—0
On the Road to Clarity: Exploring Explainable AI for World Models in a Driver Assistance System—0
On the Robustness of CountSketch to Adaptive Inputs—0
On the Suboptimality of Proximal Gradient Descent for ^0 Sparse Approximation—0
On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification—0
On the Use of Interpretable Machine Learning for the Management of Data Quality—0
On the Use of the Kantorovich-Rubinstein Distance for Dimensionality Reduction—0
On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit—0
On the use of Wasserstein metric in topological clustering of distributional data—0
OPDR: Order-Preserving Dimension Reduction for Semantic Embedding of Multimodal Scientific Data—0
Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti—0
Optimal approximate matrix product in terms of stable rank—0
Optimal estimation of sparse topic models—0
Optimal high-precision shadow estimation—0
Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform—0
Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measures—0
Optimal learning rates for Kernel Conjugate Gradient regression—0
Optimal Projections for Classification with Naive Bayes—0
Optimal Sparse Singular Value Decomposition for High-dimensional High-order Data—0
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters—0
Optimal terminal dimensionality reduction in Euclidean space—0
Optimal Transport for Brain-Image Alignment: Unveiling Redundancy and Synergy in Neural Information Processing—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