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 1541–1550 of 3304 papers

TitleStatusHype
Sirius: Visualization of Mixed Features as a Mutual Information Network GraphCode0
Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reductionCode1
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical ProjectionsCode1
Evaluating Meta-Feature Selection for the Algorithm Recommendation ProblemCode0
Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments—0
Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey—0
Shape-Preserving Dimensionality Reduction : An Algorithm and Measures of Topological Equivalence—0
A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection—0
A Discussion On the Validity of Manifold Learning—0
Matrix factorisation and the interpretation of geodesic distanceCode0
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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