SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1851–1860 of 3304 papers

TitleStatusHype
ECG Beats Fast Classification Base on Sparse DictionariesCode0
Dimension Reduction for High Dimensional Vector Autoregressive Models—0
Improving Problem Identification via Automated Log Clustering using Dimensionality Reduction—0
A Perturbation-Based Kernel Approximation FrameworkCode0
Screening Rules and its Complexity for Active Set Identification—0
Going Beyond T-SNE: Exposing whatlies in Text EmbeddingsCode1
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
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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