SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 431–440 of 3304 papers

TitleStatusHype
Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks—0
Generalizable Spectral Embedding with an Application to UMAP—0
Lee and Seung (2000)'s Algorithms for Non-negative Matrix Factorization: A Supplementary Proof Guide—0
Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning ApproachCode0
On the dimension of pullback attractors in recurrent neural networks—0
HOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting—0
Enhancing UAV Path Planning Efficiency Through Accelerated Learning—0
Refusal Behavior in Large Language Models: A Nonlinear PerspectiveCode0
Exploring Narrative Clustering in Large Language Models: A Layerwise Analysis of BERT—0
PRKAN: Parameter-Reduced Kolmogorov-Arnold Networks—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