SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1451–1460 of 3304 papers

TitleStatusHype
A Probabilistic Graph Coupling View of Dimension Reduction—0
Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery—0
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection—0
Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection—0
2D+3D facial expression recognition via embedded tensor manifold regularization—0
Approximate Bayesian Computation with Domain Expert in the LoopCode0
Semi-Supervised Quantile Estimation: Robust and Efficient Inference in High Dimensional Settings—0
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models—0
Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey—0
Error-Correcting Neural Networks for Two-Dimensional Curvature Computation in the Level-Set MethodCode0
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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