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 961–970 of 3304 papers

TitleStatusHype
Information Processing by Neuron Populations in the Central Nervous System: Mathematical Structure of Data and Operations—0
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence—0
Are We Using Autoencoders in a Wrong Way?Code0
An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognitionCode0
Online Adaptive Mahalanobis Distance Estimation—0
Why do universal adversarial attacks work on large language models?: Geometry might be the answer—0
Dimensionality Reduction Using pseudo-Boolean polynomials For Cluster Analysis—0
Optimal Projections for Discriminative Dictionary Learning using the JL-lemmaCode0
Sparse Models for Machine Learning—0
Class-constrained t-SNE: Combining Data Features and Class ProbabilitiesCode0
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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