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 701–710 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
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
Contextual Categorization Enhancement through LLMs Latent-Space—0
Decoder Decomposition for the Analysis of the Latent Space of Nonlinear Autoencoders With Wind-Tunnel Experimental DataCode0
Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis—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