SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1321–1330 of 3304 papers

TitleStatusHype
A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images—0
Firing Rate Dynamics in Recurrent Spiking Neural Networks with Intrinsic and Network Heterogeneity—0
Firm Heterogeneity and Macroeconomic Fluctuations: a Functional VAR model—0
First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces—0
First-order bifurcation detection for dynamic complex networks—0
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images—0
Computing Approximate _p Sensitivities—0
A Novel method for Schizophrenia classification using nonlinear features and neural networks—0
Fitting a Simplicial Complex using a Variation of k-means—0
Efficient Learning and Planning with Compressed Predictive States—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