SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2351–2360 of 3304 papers

TitleStatusHype
Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold—0
ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing—0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"—0
Machine Learning With Feature Selection Using Principal Component Analysis for Malware Detection: A Case Study—0
Distance metric learning based on structural neighborhoods for dimensionality reduction and classification performance improvement—0
Can Genetic Programming Do Manifold Learning Too?—0
License Plate Recognition with Compressive Sensing Based Feature Extraction—0
Principal Model Analysis Based on Partial Least Squares—0
Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold—0
Minimum description length as an objective function for non-negative matrix factorization—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