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 601–610 of 3304 papers

TitleStatusHype
A flexible Bayesian g-formula for causal survival analyses with time-dependent confoundingCode0
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein—0
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial LibrariesCode0
Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data—0
GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component AnalysisCode0
Run-to-Run Control With Bayesian Optimization for Soft Landing of Short-Stroke Reluctance Actuators—0
A Big Data Architecture for Early Identification and Categorization of Dark Web SitesCode0
Accelerating hyperbolic t-SNE—0
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces—0
Full-dimensional characterisation of time-warped spike-time stimulus-response distribution geometries—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