SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1021–1030 of 3304 papers

TitleStatusHype
Factor-augmented sparse MIDAS regressions with an application to nowcasting—0
On the use of the Gram matrix for multivariate functional principal components analysisCode0
Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and SparsificationCode0
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
Application of Deep Learning for Predictive Maintenance of Oilfield Equipment—0
Vision Transformer with Attention Map Hallucination and FFN Compaction—0
Nonlinear Feature Aggregation: Two Algorithms driven by Theory—0
Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction—0
Enhanced Sampling with Machine Learning: A Review—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