SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 401–410 of 3304 papers

TitleStatusHype
Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN—0
Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image SegmentationCode0
On Probabilistic Embeddings in Optimal Dimension Reduction—0
Principal component analysis balancing prediction and approximation accuracy for spatial dataCode0
Feature Clock: High-Dimensional Effects in Two-Dimensional Plots—0
Automated Classification of Dry Bean Varieties Using XGBoost and SVM Models—0
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks—0
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text SpatializationsCode0
Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemiaCode0
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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