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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 831–840 of 3304 papers

TitleStatusHype
Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX)—0
Learning Nonautonomous Systems via Dynamic Mode Decomposition—0
Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs—0
Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNECode0
Factor-augmented sparse MIDAS regressions with an application to nowcasting—0
Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and SparsificationCode0
On the use of the Gram matrix for multivariate functional principal components analysisCode0
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequencesCode1
Relating tSNE and UMAP to Classical Dimensionality Reduction—0
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)Code0
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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