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 3201–3225 of 3304 papers

TitleStatusHype
Optimal High-order Tensor SVD via Tensor-Train Orthogonal IterationCode0
Supporting Multi-point Fan Design with Dimension ReductionCode0
Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemiaCode0
Principal component analysis balancing prediction and approximation accuracy for spatial dataCode0
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity AnalysisCode0
Using Space-Filling Curves and Fractals to Reveal Spatial and Temporal Patterns in Neuroimaging DataCode0
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density EstimationCode0
SparCA: Sparse Compressed Agglomeration for Feature Extraction and Dimensionality ReductionCode0
Sparse and Functional Principal Components AnalysisCode0
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality ReductionCode0
Rerouting Connection: Hybrid Computer Vision Analysis Reveals Visual Similarity Between Indus and Tibetan-Yi Corridor Writing SystemsCode0
Gradient-based Sparse Principal Component Analysis with Extensions to Online LearningCode0
On the cross-validation bias due to unsupervised pre-processingCode0
Unsupervised Functional Data Analysis via Nonlinear Dimension ReductionCode0
Reservoir computing approaches for representation and classification of multivariate time seriesCode0
Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality ReductionCode0
Machine learning algorithms for three-dimensional mean-curvature computation in the level-set methodCode0
A Big Data Architecture for Early Identification and Categorization of Dark Web SitesCode0
Machine Learning Based Forward Solver: An Automatic Framework in gprMaxCode0
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorizationCode0
Convex Formulations for Fair Principal Component AnalysisCode0
Graph Convolutional Networks Meet with High Dimensionality ReductionCode0
UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised LearningCode0
DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning ModelsCode0
Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised LearningCode0
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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