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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 876–900 of 3304 papers

TitleStatusHype
Contrastive inverse regression for dimension reduction—0
Learning low-dimensional dynamics from whole-brain data improves task capture—0
Functional sufficient dimension reduction through information maximization with application to classification—0
State Representation Learning Using an Unbalanced AtlasCode0
Spectral Clustering via Orthogonalization-Free MethodsCode0
A Note on Dimensionality Reduction in Deep Neural Networks using Empirical Interpolation Method—0
Intrinsic statistical separation of subpopulations in heterogeneous collective motion via dimensionality reductionCode0
Small-data Reduced Order Modeling of Chaotic Dynamics through SyCo-AE: Synthetically Constrained Autoencoders—0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—0
ActUp: Analyzing and Consolidating tSNE and UMAPCode1
Agile gesture recognition for capacitive sensing devices: adapting on-the-job—0
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional DatasetsCode1
Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reduction—0
Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing—0
High-Dimensional Smoothed Entropy Estimation via Dimensionality Reduction—0
K-SpecPart: Supervised embedding algorithms and cut overlay for improved hypergraph partitioning—0
A Heath-Jarrow-Morton framework for energy markets: a pragmatic approach—0
Wasserstein Dictionaries of Persistence Diagrams—0
The Power of Typed Affine Decision Structures: A Case Study—0
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability—0
Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes—0
A Method for Classifying Snow Using Ski-Mounted Strain Sensors—0
Functional Diffusion MapsCode0
On Manifold Learning in Plato's Cave: Remarks on Manifold Learning and Physical PhenomenaCode0
Time delay multi-feature correlation analysis to extract subtle dependencies from EEG signals—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