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 2051–2060 of 3304 papers

TitleStatusHype
Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction—0
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images—0
Single-Pass PCA of Large High-Dimensional Data—0
Single-Sequence-Based Protein Secondary Structure Prediction using One-Hot and Chemical Encodings of Amino Acids—0
S-Isomap++: Multi Manifold Learning from Streaming Data—0
Size matters for OTC market makers: general results and dimensionality reduction techniques—0
Sketched Subspace Clustering—0
Skip-Gram − Zipf + Uniform = Vector Additivity—0
SKYNET: an efficient and robust neural network training tool for machine learning in astronomy—0
Small-data Reduced Order Modeling of Chaotic Dynamics through SyCo-AE: Synthetically Constrained Autoencoders—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