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 2151–2160 of 3304 papers

TitleStatusHype
Kernel PCA for multivariate extremes—0
Kernel Regression with Sparse Metric Learning—0
Kernel Scaling for Manifold Learning and Classification—0
KIDS: kinematics-based (in)activity detection and segmentation in a sleep case study—0
K-means Derived Unsupervised Feature Selection using Improved ADMM—0
K-Nearest-Neighbors Induced Topological PCA for scRNA Sequence Data Analysis—0
Knowledge Base Index Compression via Dimensionality and Precision Reduction—0
Knowledge Discovery In Nanophotonics Using Geometric Deep Learning—0
Knowledge Discovery using Unsupervised Cognition—0
Kronecker PCA Based Spatio-Temporal Modeling of Video for Dismount Classification—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