SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2071–2080 of 3304 papers

TitleStatusHype
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
Kernelized Multiview Subspace Analysis by Self-weighted Learning—0
GRASPEL: Graph Spectral Learning at Scale—0
A Fully Convolutional Network for MR Fingerprinting—0
Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy—0
TMI: Thermodynamic inference of data manifolds—0
Gradient-based Sparse Principal Component Analysis with Extensions to Online LearningCode0
Event detection in Colombian security Twitter news using fine-grained latent topic analysis—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