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 1651–1660 of 3304 papers

TitleStatusHype
Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization—0
Efficient and Scalable Batch Bayesian Optimization Using K-Means—0
Bayesian tomography using polynomial chaos expansion and deep generative networks—0
Efficient Binary Embedding of Categorical Data using BinSketch—0
Efficient channel charting via phase-insensitive distance computation—0
Efficient Contextual Representation Learning Without Softmax Layer—0
Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs—0
Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products—0
Efficient Fair Principal Component Analysis—0
Efficient GPU implementation of randomized SVD and its applications—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