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 1461–1470 of 3304 papers

TitleStatusHype
Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning—0
Knowledge Base Index Compression via Dimensionality and Precision Reduction—0
Encoding large information structures in linear algebra and statistical modelsCode0
Systematic analysis reveals key microRNAs as diagnostic and prognostic factors in progressive stages of lung cancer—0
Feature Space Hijacking Attacks against Differentially Private Split Learning—0
An Introduction to Autoencoders—0
Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication—0
Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra—0
Machine-Learning the Classification of Spacetimes—0
Modelling matrix time series via a tensor CP-decomposition—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