SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 811–820 of 3304 papers

TitleStatusHype
Improving Surrogate Model Robustness to Perturbations for Dynamical Systems Through Machine Learning and Data Assimilation—0
Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning—0
Company2Vec -- German Company Embeddings based on Corporate Websites—0
Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text SpatializationCode0
Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans—0
Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural NetworksCode0
Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data—0
On Sufficient Graphical Models—0
Bayesian tomography using polynomial chaos expansion and deep generative networks—0
Differential Privacy for Clustering Under Continual Observation—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