SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 751–760 of 3304 papers

TitleStatusHype
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems—0
A Kernelization-Based Approach to Nonparametric Binary Choice Models—0
A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation—0
Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data—0
Decoding Emotional Experience through Physiological Signal Processing—0
Decoding Imagined Speech and Computer Control using Brain Waves—0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images—0
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap—0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—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