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 1441–1450 of 3304 papers

TitleStatusHype
Reducing Redundancy in the Bottleneck Representation of the Autoencoders—0
Distributionally Robust Fair Principal Components via Geodesic Descents—0
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender SystemsCode0
Grassmann Stein Variational Gradient DescentCode0
A comprehensive survey on computational learning methods for analysis of gene expression data—0
A Comparison of Representation Learning Methods for Dimensionality Reduction of fMRI Scans for Classification of ADHD—0
On Manifold Hypothesis: Hypersurface Submanifold Embedding Using Osculating Hyperspheres—0
A selective review of sufficient dimension reduction for multivariate response regression—0
Dimensionality Reduction Meets Message Passing for Graph Node EmbeddingsCode0
Topology-Preserving Dimensionality Reduction via Interleaving OptimizationCode0
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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