SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 771–780 of 3304 papers

TitleStatusHype
GroupEnc: encoder with group loss for global structure preservation—0
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
Online Adaptive Mahalanobis Distance Estimation—0
An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognitionCode0
Multi-fidelity reduced-order surrogate modelingCode1
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
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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