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 871–880 of 3304 papers

TitleStatusHype
Let There Be Order: Rethinking Ordering in Autoregressive Graph GenerationCode0
Efficient Multi-Scale Attention Module with Cross-Spatial LearningCode2
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilityCode0
Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples—0
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations—0
Contrastive inverse regression for dimension reduction—0
Learning low-dimensional dynamics from whole-brain data improves task capture—0
Functional sufficient dimension reduction through information maximization with application to classification—0
State Representation Learning Using an Unbalanced AtlasCode0
Spectral Clustering via Orthogonalization-Free MethodsCode0
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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