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 2281–2290 of 3304 papers

TitleStatusHype
Local Shrunk Discriminant Analysis (LSDA)—0
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval—0
Logical Interpretations of Autoencoders—0
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based ROMs—0
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models—0
Lossless KV Cache Compression to 2%—0
Low-complexity 8-point DCT Approximation Based on Angle Similarity for Image and Video Coding—0
Low-complexity Point Cloud Filtering for LiDAR by PCA-based Dimension Reduction—0
Low-complexity Rounded KLT Approximation for Image Compression—0
Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach—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