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 1661–1670 of 3304 papers

TitleStatusHype
Efficient L1-Norm Principal-Component Analysis via Bit Flipping—0
Efficient Large-Scale Similarity Search Using Matrix Factorization—0
Efficient Large-Scale Urban Parking Prediction: Graph Coarsening Based on Real-Time Parking Service Capability—0
Efficient Learning and Planning with Compressed Predictive States—0
Efficiently Computing Similarities to Private Datasets—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Efficient Nearest Neighbor based Uncertainty Estimation for Natural Language Processing Tasks—0
Efficient NTK using Dimensionality Reduction—0
Efficient Optimization for Discriminative Latent Class Models—0
Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching—0
Show:102550
← PrevPage 167 of 331Next →
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