SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 2521–2530 of 3304 papers

TitleStatusHype
A Convex Sparse PCA for Feature Analysis—0
A Correspondence Analysis Framework for Author-Conference Recommendations—0
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA—0
A Critical Note on the Evaluation of Clustering Algorithms—0
A Cross Entropy test allows quantitative statistical comparison of t-SNE and UMAP representations—0
Active Learning with TensorBoard Projector—0
Active Linear Regression for _p Norms and Beyond—0
A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission—0
Adapting Speaker Embeddings for Speaker Diarisation—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot 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