SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 241–250 of 3304 papers

TitleStatusHype
A determinantal point process for column subset selection—0
A description length approach to determining the number of k-means clusters—0
Analysis and mining of low-carbon and energy-saving tourism data characteristics based on machine learning algorithm—0
Analysis and Visualization of Deep Neural Networks in Device-Free Wi-Fi Indoor Localization—0
Analysis of Cellular Feature Differences of Astrocytomas with Distinct Mutational Profiles Using Digitized Histopathology Images—0
Analysis of Evolving Cortical Neuronal Networks Using Visual Informatics—0
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence—0
Analytic Bilinear Appearance Subspace Construction for Modeling Image Irradiance under Natural Illumination and Non-Lambertian Reflectance—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems—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