SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 521–530 of 3304 papers

TitleStatusHype
Causal learning with sufficient statistics: an information bottleneck approach—0
Classic machine learning methods—0
Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images—0
Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics—0
A Cross Entropy test allows quantitative statistical comparison of t-SNE and UMAP representations—0
Applications of machine learning to predict seasonal precipitation for East Africa—0
Application Research On Real-Time Perception Of Device Performance Status—0
A Harmony Search Based Wrapper Feature Selection Method for Holistic Bangla word Recognition—0
A Category Space Approach to Supervised Dimensionality Reduction—0
Application of Symmetric Uncertainty and Mutual Information to Dimensionality Reduction and Classification of Hyperspectral Images—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