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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2151–2175 of 3304 papers

TitleStatusHype
A Quotient Space Formulation for Generative Statistical Analysis of Graphical DataCode0
Manifold Fitting in Ambient Space—0
Siamese Neural Networks for Wireless Positioning and Channel Charting—0
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings—0
Capacity Preserving Mapping for High-dimensional Data VisualizationCode0
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
Estimating covariance and precision matrices along subspaces—0
Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding—0
Function Preserving Projection for Scalable Exploration of High-Dimensional DataCode0
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario—0
Dimension Estimation Using Autoencoders—0
The space complexity of inner product filters—0
Manifold Fitting under Unbounded NoiseCode0
Application of Fuzzy Clustering for Text Data Dimensionality Reduction—0
Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs—0
Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation—0
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold—0
Laplacian Matrix for Dimensionality Reduction and Clustering—0
Data Mapping and Finite Difference LearningCode0
Semantic Relatedness Based Re-ranker for Text SpottingCode0
PixelHop: A Successive Subspace Learning (SSL) Method for Object ClassificationCode0
Knowledge Discovery In Nanophotonics Using Geometric Deep Learning—0
Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecastingCode0
Multi-Perspective, Simultaneous EmbeddingCode0
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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