SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1041–1050 of 3304 papers

TitleStatusHype
Yet Another Algorithm for Supervised Principal Component Analysis: Supervised Linear Centroid-Encoder—0
Multiscale Flow for Robust and Optimal Cosmological Analysis—0
Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models—0
SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving—0
Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic Optimization—0
Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization—0
ShaRP: Shape-Regularized Multidimensional ProjectionsCode0
Dictionary Learning under Symmetries via Group Representations—0
Label Embedding via Low-Coherence Matrices—0
Dimensionality Reduction for General KDE Mode Finding—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