SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 711–720 of 3304 papers

TitleStatusHype
Modular Boundaries in Recurrent Neural NetworksCode2
Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam PredictionCode0
Gauge-optimal approximate learning for small data classification problems—0
Neural Stress Fields for Reduced-order Elastoplasticity and Fracture—0
Low-Dimensional Gradient Helps Out-of-Distribution Detection—0
Instance-wise Linearization of Neural Network for Model Interpretation—0
Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era—0
K-Nearest-Neighbors Induced Topological PCA for scRNA Sequence Data Analysis—0
Noise-robust latent vector reconstruction in ptychography using deep generative models—0
Layered Models can "Automatically" Regularize and Discover Low-Dimensional Structures via Feature LearningCode0
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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