SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1271–1280 of 3304 papers

TitleStatusHype
Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material—0
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
Data Discovery Using Lossless Compression-Based Sparse Representation—0
Data Dimension Reduction makes ML Algorithms efficient—0
A predictive physics-aware hybrid reduced order model for reacting flows—0
A Hybrid Quantum Classical Pipeline for X Ray Based Fracture Diagnosis—0
Data Augmentation For Label Enhancement—0
Data augmentation and feature selection for automatic model recommendation in computational physics—0
DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection—0
Approximation of Functions over Manifolds: A Moving Least-Squares Approach—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