SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 561–570 of 3304 papers

TitleStatusHype
A mechanism-driven reinforcement learning framework for shape optimization of airfoils—0
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time SeriesCode0
Explaining Genetic Programming Trees using Large Language Models—0
Black-Box k-to-1-PCA Reductions: Theory and Applications—0
Permutation invariant functions: statistical tests, density estimation, and computationally efficient embedding—0
Density-based Isometric Mapping—0
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques—0
ELA: Efficient Local Attention for Deep Convolutional Neural Networks—0
Γ-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data—0
Dimensionality reduction techniques to support insider trading detection—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