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 1911–1920 of 3304 papers

TitleStatusHype
A Perturbation-Based Kernel Approximation FrameworkCode0
Dimension Reduction for High Dimensional Vector Autoregressive Models—0
Improving Problem Identification via Automated Log Clustering using Dimensionality Reduction—0
Screening Rules and its Complexity for Active Set Identification—0
An approximate solution for options market-making in high dimension—0
Transform Quantization for CNN (Convolutional Neural Network) Compression—0
Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images—0
GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model—0
Exact and Approximation Algorithms for Sparse PCACode0
Clustering small datasets in high-dimension by random projection—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