SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2091–2100 of 3304 papers

TitleStatusHype
Solving Interpretable Kernel Dimensionality Reduction—0
Precision-Recall Balanced Topic Modelling—0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels—0
Distributed estimation of principal support vector machines for sufficient dimension reduction—0
Logical Interpretations of Autoencoders—0
Matrix Normal PCA for Interpretable Dimension Reduction and Graphical Noise Modeling—0
GRASPEL: Graph Spectral Learning at Scale—0
Kernelized Multiview Subspace Analysis by Self-weighted Learning—0
Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy—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