SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 491–500 of 3304 papers

TitleStatusHype
Reduced-Rank Multi-objective Policy Learning and Optimization—0
DIRESA, a distance-preserving nonlinear dimension reduction technique based on regularized autoencoders—0
GARA: A novel approach to Improve Genetic Algorithms' Accuracy and Efficiency by Utilizing Relationships among Genes—0
CBMAP: Clustering-based manifold approximation and projection for dimensionality reductionCode1
Dynamical Mode Recognition of Coupled Flame Oscillators by Supervised and Unsupervised Learning Approaches—0
On the Road to Clarity: Exploring Explainable AI for World Models in a Driver Assistance System—0
Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems—0
Decoder Decomposition for the Analysis of the Latent Space of Nonlinear Autoencoders With Wind-Tunnel Experimental DataCode0
Contextual Categorization Enhancement through LLMs Latent-Space—0
Machine-Learned Closure of URANS for Stably Stratified Turbulence: Connecting Physical Timescales & Data Hyperparameters of Deep Time-Series Models—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