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 1876–1900 of 3304 papers

TitleStatusHype
Random matrix approach to estimation of high-dimensional factor models—0
Random Maxout Features—0
Random Positive-Only Projections: PPMI-Enabled Incremental Semantic Space Construction—0
Random Projection Estimation of Discrete-Choice Models with Large Choice Sets—0
Random Projections for Improved Adversarial Robustness—0
Random Projections for k-means Clustering—0
Random Projections for Linear Support Vector Machines—0
Random Projections for Manifold Learning—0
Random projections of random manifolds—0
Random Subspace Local Projections—0
Ranking Entities in the Age of Two Webs, an Application to Semantic Snippets—0
Rank Reduction Autoencoders—0
RdimKD: Generic Distillation Paradigm by Dimensionality Reduction—0
Rdimtools: An R package for Dimension Reduction and Intrinsic Dimension Estimation—0
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras—0
Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques—0
Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models—0
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models—0
Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding—0
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval—0
Reconstructing Big Semantic Similarity Networks—0
Recovery of Linear Components: Reduced Complexity Autoencoder Designs—0
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation—0
Recurrent neural networks learn robust representations by dynamically balancing compression and expansion—0
Reduced Basis Decomposition: a Certified and Fast Lossy Data Compression Algorithm—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