SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1401–1410 of 3304 papers

TitleStatusHype
Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain—0
Free Lunch for Gait Recognition: A Novel Relation Descriptor—0
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information—0
Generalized Principal Component Analysis—0
Fréchet Cumulative Covariance Net for Deep Nonlinear Sufficient Dimension Reduction with Random Objects—0
Supervised tensor decomposition with features on multiple modes—0
FRE: A Fast Method For Anomaly Detection And Segmentation—0
Generative adversarial learning with optimal input dimension and its adaptive generator architecture—0
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—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