SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2101–2110 of 3304 papers

TitleStatusHype
Specific language impairment (SLI) detection pipeline from transcriptions of spontaneous narratives—0
Spectral convergence of diffusion maps: improved error bounds and an alternative normalisation—0
Spectral Convergence of the connection Laplacian from random samples—0
Spectral Diffusion Processes—0
Spectral Echolocation via the Wave Embedding—0
Spectral estimation from simulations via sketching—0
Spectral feature scaling method for supervised dimensionality reduction—0
Spectral independent component analysis with noise modeling for M/EEG source separation—0
Spectral Learning on Matrices and Tensors—0
Spectrally-Corrected and Regularized Linear Discriminant Analysis for Spiked Covariance Model—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