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 1851–1875 of 3304 papers

TitleStatusHype
Going Beyond T-SNE: Exposing whatlies in Text Embeddings—0
Towards Measuring Place Function Similarity at Fine Spatial Granularity with Trajectory Embedding—0
RRScell method for automated single-cell profiling of multiplexed immunofluorescence cancer tissue—0
Deep generative LDACode0
The Mathematical Foundations of Manifold Learning—0
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection—0
Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques—0
Deep Manifold Transformation for Nonlinear Dimensionality Reduction—0
Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder—0
Nested Grassmannians for Dimensionality Reduction with ApplicationsCode0
Improving the generalization of network based relative pose regression: dimension reduction as a regularizer—0
Geometry-Aware Hamiltonian Variational Auto-Encoder—0
Tensor Train Random Projection—0
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks—0
Auto-Encoding Variational Bayes for Inferring Topics and VisualizationCode0
Statistical guarantees for generative models without domination—0
Product Manifold LearningCode0
Sufficient dimension reduction for classification using principal optimal transport directionCode0
Prediction of daily maximum ozone levels using Lasso sparse modeling method—0
Interactive Latent Interpolation on MNIST DatasetCode0
Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank EstimationCode0
Mycorrhiza: Genotype Assignment usingPhylogenetic NetworksCode0
Causal learning with sufficient statistics: an information bottleneck approach—0
0-dimensional Homology Preserving Dimensionality Reduction with TopoMap—0
Circular Coordinate Methods with Generalized Penalty Functions—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