SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2901–2910 of 3304 papers

TitleStatusHype
Classic machine learning methods—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—0
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data—0
Classification of Cervical Cancer Dataset—0
Classification of EEG Signals using Genetic Programming for Feature Construction—0
Classification of high-dimensional data with spiked covariance matrix structure—0
Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality—0
Classification with Repulsion Tensors: A Case Study on Face Recognition—0
Class Mean Vector Component and Discriminant Analysis—0
Class-Wise Principal Component Analysis for hyperspectral image feature extraction—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