SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1210 of 3304 papers

TitleStatusHype
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—0
Finding Rule-Interpretable Non-Negative Data Representation—0
Impact of the composition of feature extraction and class sampling in medicare fraud detection—0
Augmentation Component Analysis: Modeling Similarity via the Augmentation OverlapsCode0
AVIDA: Alternating method for Visualizing and Integrating Data—0
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time SeriesCode1
Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognitionCode0
Principal Component Analysis based frameworks for efficient missing data imputation algorithms—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
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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