SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1426–1450 of 3304 papers

TitleStatusHype
Using the left Gram matrix to cluster high dimensional data—0
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods—0
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks—0
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
Toward Unsupervised Test Scenario Extraction for Automated Driving Systems from Urban Naturalistic Road Traffic Data—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
Measuring disentangled generative spatio-temporal representationCode0
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty—0
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems—0
Non-Linear Spectral Dimensionality Reduction Under Uncertainty—0
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA—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
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
Topology-Preserving Dimensionality Reduction via Interleaving OptimizationCode0
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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