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
Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal ParticlesCode0
Local Explanation of Dimensionality ReductionCode0
Representative period selection for power system planning using autoencoder-based dimensionality reduction—0
BYTECOVER2: TOWARDS DIMENSIONALITY REDUCTION OF LATENT EMBEDDING FOR EFFICIENT COVER SONG IDENTIFICATION—0
On the Use of Dimension Reduction or Signal Separation Methods for Nitrogen River Pollution Source Identification—0
Trainable Compound Activation Functions for Machine Learning—0
Spherical Rotation Dimension Reduction with Geometric Loss FunctionsCode0
Dimension Reduction for time series with Variational AutoEncoders—0
Capturing the Denoising Effect of PCA via Compression Ratio—0
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations—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