SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1771–1780 of 3304 papers

TitleStatusHype
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach—0
Extension of PCA to Higher Order Data Structures: An Introduction to Tensors, Tensor Decompositions, and Tensor PCA—0
Extração e Classificação de Características Radiômicas em Gliomas de Baixo Grau para Análise da Codeleção 1p/19q—0
Extracting Geography from Trade Data—0
Extracting grid characteristics from spatially distributed place cell inputs using non-negative PCA—0
Extracting lexico-semantic relations from specialized corpora using a word space model (Analyse distributionnelle de corpus sp\'ecialis\'es pour l'identification de relations lexico-s\'emantiques) [in French]—0
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations—0
Extreme compression of sentence-transformer ranker models: faster inference, longer battery life, and less storage on edge devices—0
Extreme Dimension Reduction for Handling Covariate Shift—0
Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning—0
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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