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 2221–2230 of 3304 papers

TitleStatusHype
Learning low dimensional word based linear classifiers using Data Shared Adaptive Bootstrap Aggregated Lasso with application to IMDb data—0
Learning Manifolds from Non-stationary Streaming Data—0
Learning Mixtures of Arbitrary Distributions over Large Discrete Domains—0
Learning Multiple Non-linear Sub-spaces Using K-RBMs—0
Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains—0
Learning Nonautonomous Systems via Dynamic Mode Decomposition—0
Learning Optimal Deep Projection of ^18F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes—0
Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins—0
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders—0
Learning Restricted Boltzmann Machines via Influence Maximization—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