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 1951–1960 of 3304 papers

TitleStatusHype
RMFGP: Rotated Multi-fidelity Gaussian process with Dimension Reduction for High-dimensional Uncertainty Quantification—0
Robust Co-occurrence Quantification for Lexical Distributional Semantics—0
Robust Discriminative Clustering with Sparse Regularizers—0
Robust factored principal component analysis for matrix-valued outlier accommodation and detection—0
Robust Feature Extraction to Utterance Fluctuation of Articulation Disorders Based on Random Projection—0
Robust High-Dimensional Linear Regression—0
Robust Kronecker Component Analysis—0
Robust learning of low-dimensional dynamics from large neural ensembles—0
Robust Linear Classification from Limited Training Data—0
Restricted Minimum Error Entropy Criterion for Robust Classification—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