SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1741–1750 of 3304 papers

TitleStatusHype
Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions—0
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features—0
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series—0
Out-of-sample extension of graph adjacency spectral embedding—0
Outperforming Word2Vec on Analogy Tasks with Random Projections—0
Ovarian Cancer Detection based on Dimensionality Reduction Techniques and Genetic Algorithm—0
Overlapping Sliced Inverse Regression for Dimension Reduction—0
A semi-supervised learning using over-parameterized regression—0
Pair Distance Distribution: A Model of Semantic Representation—0
Parallel Coordinates for Discovery of Interpretable Machine Learning Models—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