SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1231–1240 of 3304 papers

TitleStatusHype
Broadband Beamforming via Linear Embedding—0
Extreme compression of sentence-transformer ranker models: faster inference, longer battery life, and less storage on edge devices—0
Extreme Dimension Reduction for Handling Covariate Shift—0
Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning—0
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification—0
An Investigation of Newton-Sketch and Subsampled Newton Methods—0
Face Recognition using Curvelet Transform—0
Face Recognition using Hough Peaks extracted from the significant blocks of the Gradient Image—0
Facilitate the Parametric Dimension Reduction by Gradient Clipping—0
Faster Discovery of Faster System Configurations with Spectral Learning—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