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 2126–2150 of 3304 papers

TitleStatusHype
MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive—0
TriMap: Large-scale Dimensionality Reduction Using TripletsCode0
Specializing Word Embeddings (for Parsing) by Information BottleneckCode0
A Quotient Space Formulation for Generative Statistical Analysis of Graphical DataCode0
Manifold Fitting in Ambient Space—0
Capacity Preserving Mapping for High-dimensional Data VisualizationCode0
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings—0
Siamese Neural Networks for Wireless Positioning and Channel Charting—0
A New Covariance Estimator for Sufficient Dimension Reduction in High-Dimensional and Undersized Sample Problems—0
Netboost: Boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease—0
Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding—0
Estimating covariance and precision matrices along subspaces—0
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario—0
Function Preserving Projection for Scalable Exploration of High-Dimensional DataCode0
The space complexity of inner product filters—0
Dimension Estimation Using Autoencoders—0
Manifold Fitting under Unbounded NoiseCode0
Application of Fuzzy Clustering for Text Data Dimensionality Reduction—0
Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation—0
Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs—0
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality ReductionCode1
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold—0
Data Mapping and Finite Difference LearningCode0
Laplacian Matrix for Dimensionality Reduction and Clustering—0
PixelHop: A Successive Subspace Learning (SSL) Method for Object ClassificationCode0
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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