SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1191–1200 of 3304 papers

TitleStatusHype
A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification—0
Algorithms for Approximate Subtropical Matrix Factorization—0
Deep-learning based measurement of planetary radial velocities in the presence of stellar variability—0
Deep Learning Architecture for Motor Imaged Words—0
A simple coding for cross-domain matching with dimension reduction via spectral graph embedding—0
Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures—0
Estimation of a function of low local dimensionality by deep neural networks—0
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction—0
Algorithmic Stability and Uniform Generalization—0
Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods—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