SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1781–1790 of 3304 papers

TitleStatusHype
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification—0
Face Recognition using Curvelet Transform—0
Face Recognition using Hough Peaks extracted from the significant blocks of the Gradient Image—0
Facilitate the Parametric Dimension Reduction by Gradient Clipping—0
Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey—0
Factor-augmented sparse MIDAS regressions with an application to nowcasting—0
Factorization of Latent Variables in Distributional Semantic Models—0
Factor Network Autoregressions—0
Factors in Fashion: Factor Analysis towards the Mode—0
Factors of Transferability for a Generic ConvNet Representation—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