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 2161–2170 of 3304 papers

TitleStatusHype
K-SpecPart: Supervised embedding algorithms and cut overlay for improved hypergraph partitioning—0
L^3-SVMs: Landmarks-based Linear Local Support Vectors Machines—0
Label Embedding via Low-Coherence Matrices—0
Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction—0
Language-Assisted 3D Scene Understanding—0
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels—0
Laplacian-based Cluster-Contractive t-SNE for High Dimensional Data Visualization—0
Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey—0
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering—0
Laplacian Eigenmaps from Sparse, Noisy Similarity Measurements—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