SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 571–580 of 3304 papers

TitleStatusHype
A Novel Approach for Single Gene Selection Using Clustering and Dimensionality Reduction—0
Aggregated Deep Local Features for Remote Sensing Image Retrieval—0
A Convex Formulation for Spectral Shrunk Clustering—0
A Novel Approach for Intrinsic Dimension Estimation—0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
A Convex formulation for linear discriminant analysis—0
A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy—0
A Note on Dimensionality Reduction in Deep Neural Networks using Empirical Interpolation Method—0
A Geometric take on Metric Learning—0
Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features—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