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 2041–2050 of 3304 papers

TitleStatusHype
Signed graphs in data sciences via communicability geometry—0
Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales—0
Similarity Search with Tensor Core Units—0
Simple and Powerful Architecture for Inductive Recommendation Using Knowledge Graph Convolutions—0
Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images—0
Simple strategies for recovering inner products from coarsely quantized random projections—0
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer—0
Simple, unified analysis of Johnson-Lindenstrauss with applications—0
Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learning—0
Simultaneous Dimensionality Reduction for Extracting Useful Representations of Large Empirical Multimodal Datasets—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