SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1901–1910 of 3304 papers

TitleStatusHype
Generalized Categorization Axioms—0
Generalized notions of sparsity and restricted isometry property. Part I: A unified framework—0
Generalized Principal Component Analysis—0
Supervised tensor decomposition with features on multiple modes—0
Generalized two-dimensional linear discriminant analysis with regularization—0
Generative adversarial learning with optimal input dimension and its adaptive generator architecture—0
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey—0
Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification—0
Generative Local Metric Learning for Nearest Neighbor Classification—0
Generative Modeling: A Review—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