SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2351–2375 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
A Tangent Distance Preserving Dimensionality Reduction Algorithm—0
Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations—0
Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency—0
Distributionally Robust and Multi-Objective Nonnegative Matrix Factorization—0
Representation Transfer for Differentially Private Drug Sensitivity Prediction—0
Throttling Malware Families in 2DCode0
A Deep Learning Framework for Assessing Physical Rehabilitation ExercisesCode0
Diseño de un espacio semántico sobre la base de la Wikipedia. Una propuesta de análisis de la semántica latente para el idioma español—0
Stochastic Linear Bandits with Hidden Low Rank Structure—0
On the cross-validation bias due to unsupervised pre-processingCode0
Comparing of Term Clustering Frameworks for Modular Ontology Learning—0
Empowering individual trait prediction using interactions—0
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—0
Coupling the reduced-order model and the generative model for an importance sampling estimator—0
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problemsCode0
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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