SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1301–1325 of 3304 papers

TitleStatusHype
Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning—0
A Generic Self-Supervised Framework of Learning Invariant Discriminative Features—0
An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in PipesCode0
Fuzzy Pooling—0
A Machine-Learning-Aided Visual Analysis Workflow for Investigating Air Pollution Data—0
Visualising Multiplayer Game Spaces—0
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty—0
Measuring disentangled generative spatio-temporal representationCode0
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems—0
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA—0
Non-Linear Spectral Dimensionality Reduction Under Uncertainty—0
Reducing Redundancy in the Bottleneck Representation of the Autoencoders—0
Distributionally Robust Fair Principal Components via Geodesic Descents—0
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender SystemsCode0
Grassmann Stein Variational Gradient DescentCode0
A comprehensive survey on computational learning methods for analysis of gene expression data—0
TTS-GAN: A Transformer-based Time-Series Generative Adversarial NetworkCode2
A Comparison of Representation Learning Methods for Dimensionality Reduction of fMRI Scans for Classification of ADHD—0
On Manifold Hypothesis: Hypersurface Submanifold Embedding Using Osculating Hyperspheres—0
A selective review of sufficient dimension reduction for multivariate response regression—0
Dimensionality Reduction Meets Message Passing for Graph Node EmbeddingsCode0
Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery—0
A Probabilistic Graph Coupling View of Dimension Reduction—0
Topology-Preserving Dimensionality Reduction via Interleaving OptimizationCode0
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection—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