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 3201–3210 of 3304 papers

TitleStatusHype
Multilingual Summarization: Dimensionality Reduction and a Step Towards Optimal Term Coverage—0
Using Lexical Expansion to Learn Inference Rules from Sparse Data—0
Robust Feature Extraction to Utterance Fluctuation of Articulation Disorders Based on Random Projection—0
Borel Isomorphic Dimensionality Reduction of Data and Supervised Learning—0
Dimension Reduction via Colour Refinement—0
Making Laplacians commute—0
Toward Guaranteed Illumination Models for Non-Convex Objects—0
A non-parametric conditional factor regression model for high-dimensional input and response—0
Robust Support Vector Machines for Speaker Verification Task—0
Flexible sampling of discrete data correlations without the marginal distributions—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