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 2391–2400 of 3304 papers

TitleStatusHype
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery—0
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization—0
Group Preserving Label Embedding for Multi-Label Classification—0
bigMap: Big Data Mapping with Parallelized t-SNE—0
A determinantal point process for column subset selection—0
Random Projection in Deep Neural NetworksCode0
Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning—0
Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features—0
Nonlinear demixed component analysis for neural population data as a low-rank kernel regression problemCode0
Deep Variational Sufficient Dimensionality Reduction—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