SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 501–510 of 3304 papers

TitleStatusHype
Understanding Contrastive Learning via Gaussian Mixture Models—0
You are out of context!—0
XNB: Explainable Class-Specific NaIve-Bayes ClassifierCode0
Detection and tracking of gas plumes in LWIR hyperspectral video sequence data—0
How Do Flow Matching Models Memorize and Generalize in Sample Data Subspaces?—0
Temporal Streaming Batch Principal Component Analysis for Time Series Classification—0
Dimension reduction via score ratio matching—0
Golden Ratio-Based Sufficient Dimension Reduction—0
Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation—0
Enhancing Graph Attention Neural Network Performance for Marijuana Consumption Classification through Large-scale Augmented Granger Causality (lsAGC) Analysis of Functional MR Images—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