SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 281–290 of 3304 papers

TitleStatusHype
Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems—0
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
DMT-HI: MOE-based Hyperbolic Interpretable Deep Manifold Transformation for Unspervised Dimensionality ReductionCode1
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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