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 971–980 of 3304 papers

TitleStatusHype
Dimension Reduction via Colour Refinement—0
Dimension reduction via score ratio matching—0
Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
Dimension Reduction with Non-degrading Generalization—0
Bayesian Data Sketching for Varying Coefficient Regression Models—0
Directed Scattering for Knowledge Graph-based Cellular Signaling Analysis—0
Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction—0
Direction and Constraint in Phenotypic Evolution: Dimension Reduction and Global Proportionality in Phenotype Fluctuation and Responses—0
Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity—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