SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1561–1570 of 3304 papers

TitleStatusHype
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images—0
HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction—0
Firing Rate Dynamics in Recurrent Spiking Neural Networks with Intrinsic and Network Heterogeneity—0
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification—0
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—0
Hyperspectral Images Classification and Dimensionality Reduction using spectral interaction and SVM classifier—0
Hyperspectral images classification and Dimensionality Reduction using Homogeneity feature and mutual information—0
Hyperspectral Imaging and Analysis for Sparse Reconstruction and Recognition—0
Finding Rule-Interpretable Non-Negative Data Representation—0
Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila—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