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 1111–1120 of 3304 papers

TitleStatusHype
Enhancing the conformal predictability of context-aware recommendation systems by using Deep Autoencoders—0
Elliptical modeling and pattern analysis for perturbation models and classfication—0
Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth—0
Embedding Compression for Efficient Re-Identification—0
Embedding Feature Selection for Large-scale Hierarchical Classification—0
Embedding Functional Data: Multidimensional Scaling and Manifold Learning—0
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence—0
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature—0
EMC2A-Net: An Efficient Multibranch Cross-channel Attention Network for SAR Target Classification—0
Ensembles of Classifiers based on Dimensionality Reduction—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