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 2641–2650 of 3304 papers

TitleStatusHype
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
Analyzing movies to predict their commercial viability for producers—0
Exact Cluster Recovery via Classical Multidimensional Scaling—0
An Analysis of the t-SNE Algorithm for Data Visualization—0
An approximate solution for options market-making in high dimension—0
An Attack on Facial Soft-biometric Privacy Enhancement—0
An Augmented Translation Technique for low Resource language pair: Sanskrit to Hindi translation—0
An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification—0
An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion—0
An automated approach for task evaluation using EEG signals—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