SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 3171–3180 of 3304 papers

TitleStatusHype
An adaptive block based integrated LDP,GLCM,and Morphological features for Face Recognition—0
Face Recognition using Hough Peaks extracted from the significant blocks of the Gradient Image—0
Robust learning of low-dimensional dynamics from large neural ensembles—0
Probabilistic Principal Geodesic Analysis—0
Efficient Learning and Planning with Compressed Predictive States—0
Compressive Feature Learning—0
Robust Transfer Principal Component Analysis with Rank Constraints—0
Dimensionality reduction for click-through rate prediction: Dense versus sparse representation—0
Auto-adaptative Laplacian Pyramids for High-dimensional Data Analysis—0
Toward a unified theory of sparse dimensionality reduction in Euclidean space—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