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 1701–1750 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
Optimization of embeddings storage for RAG systems using quantization and dimensionality reduction techniques—0
Optimized Projection for Sparse Representation Based Classification—0
Optimizing Feature Selection with Genetic Algorithms: A Review of Methods and Applications—0
Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition—0
Optimizing Offer Sets in Sub-Linear Time—0
Optimizing the Information Retrieval Trade-off in Data Visualization Using -Divergence—0
Optimizing Unlicensed Band Spectrum Sharing With Subspace-Based Pareto Tracing—0
Order Embeddings from Merged Ontologies using Sketching—0
Order-Preserving Wasserstein Discriminant Analysis—0
Ordinal Distance Metric Learning with MDS for Image Ranking—0
Ordonnancement d'entités pour la rencontre du web des documents et du web des données—0
OSACT4 Shared Task on Offensive Language Detection: Intensive Preprocessing-Based Approach—0
OutCenTR: A novel semi-supervised framework for predicting exploits of vulnerabilities in high-dimensional datasets—0
Outlier Detection for Robust Multi-dimensional Scaling—0
Outlier Detection In Large-scale Traffic Data By Naïve Bayes Method and Gaussian Mixture Model Method—0
Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions—0
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features—0
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series—0
Out-of-sample extension of graph adjacency spectral embedding—0
Outperforming Word2Vec on Analogy Tasks with Random Projections—0
Ovarian Cancer Detection based on Dimensionality Reduction Techniques and Genetic Algorithm—0
Overlapping Sliced Inverse Regression for Dimension Reduction—0
A semi-supervised learning using over-parameterized regression—0
Pair Distance Distribution: A Model of Semantic Representation—0
Parallel Coordinates for Discovery of Interpretable Machine Learning Models—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