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Dimensionality Reduction

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

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Papers

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Showing 851–860 of 3304 papers

TitleStatusHype
Federated Multilinear Principal Component Analysis with Applications in Prognostics—0
A quantitative fusion strategy of stock picking and timing based on Particle Swarm Optimized-Back Propagation Neural Network and Multivariate Gaussian-Hidden Markov Model—0
Speeding up astrochemical reaction networks with autoencoders and neural ODEsCode0
Economic Forecasts Using Many Noises—0
A Robust and Efficient Boundary Point Detection Method by Measuring Local Direction Dispersion—0
k* Distribution: Evaluating the Latent Space of Deep Neural Networks using Local Neighborhood AnalysisCode0
A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systems—0
Interpretability Illusions in the Generalization of Simplified Models—0
Dimensionality Reduction and Dynamical Mode Recognition of Circular Arrays of Flame Oscillators Using Deep Neural Network—0
Geometric Data-Driven Dimensionality Reduction in MPC with Guarantees—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