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 2271–2280 of 3304 papers

TitleStatusHype
Towards a Theoretical Analysis of PCA for Heteroscedastic Data—0
Towards Basque Oral Poetry Analysis: A Machine Learning Approach—0
Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case—0
Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis—0
Towards glass-box CNNs—0
Style Transfer with Time Series: Generating Synthetic Financial Data—0
Towards Making High Dimensional Distance Metric Learning Practical—0
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE—0
Towards Robust Cross-Domain Domain Adaptation for Part-of-Speech Tagging—0
Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations—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