SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1341–1350 of 3304 papers

TitleStatusHype
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
Scalable semi-supervised dimensionality reduction with GPU-accelerated EmbedSOMCode1
Modelling matrix time series via a tensor CP-decomposition—0
Dimensionality reduction for prediction: Application to Bitcoin and Ethereum—0
Exact Post-selection Inference For Tracking S&P500—0
Uniform-in-Phase-Space Data Selection with Iterative Normalizing FlowsCode0
Nonnegative OPLS for Supervised Design of Filter Banks: Application to Image and Audio Feature Extraction—0
Manifold learning via quantum dynamics—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