SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1051–1060 of 3304 papers

TitleStatusHype
A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information—0
A Faster Approach to Spiking Deep Convolutional Neural Networks—0
Gravitational Dimensionality Reduction Using Newtonian Gravity and Einstein's General RelativityCode0
Hybridization of filter and wrapper approaches for the dimensionality reduction and classification of hyperspectral images—0
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows—0
Supervised classification methods applied to airborne hyperspectral images: Comparative study using mutual information—0
Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier—0
A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines—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
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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