SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1221–1230 of 3304 papers

TitleStatusHype
Supervised classification methods applied to airborne hyperspectral images: Comparative study using mutual information—0
A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines—0
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation—0
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images—0
A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images—0
A new band selection approach based on information theory and support vector machine for hyperspectral images reduction and classification—0
A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy—0
Hyperspectral images classification and Dimensionality Reduction using Homogeneity feature and mutual information—0
New wrapper method based on normalized mutual information for dimension reduction and classification of hyperspectral images—0
NeuroMapper: In-browser Visualizer for Neural Network TrainingCode0
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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