SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2091–2100 of 3304 papers

TitleStatusHype
In search of the most efficient and memory-saving visualization of high dimensional data—0
An Empirical Study on Fault Detection and Root Cause Analysis of Indium Tin Oxide Electrodes by Processing S-parameter Patterns—0
InstanceBEV: Unifying Instance and BEV Representation for Global Modeling—0
Instance Space Analysis for the Car Sequencing Problem—0
Instance-wise Linearization of Neural Network for Model Interpretation—0
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks—0
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks—0
Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data—0
Integrative analysis reveals disrupted pathways regulated by microRNAs in cancer—0
Adaptive Weighted Multi-View Clustering—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