SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1591–1600 of 3304 papers

TitleStatusHype
A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction—0
Image retrieval method based on CNN and dimension reduction—0
Finding Real-World Orbital Motion Laws from Data—0
Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach—0
Computation of the Maximum Likelihood estimator in low-rank Factor Analysis—0
Computational Techniques in Multispectral Image Processing: Application to the Syriac Galen Palimpsest—0
Impact of the composition of feature extraction and class sampling in medicare fraud detection—0
Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons—0
A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines—0
Computational Graph Completion—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