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 3281–3290 of 3304 papers

TitleStatusHype
Constraint matrix factorization for space variant PSFs field restorationCode0
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed SystemsCode0
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future PerspectivesCode0
High Dimensional Bayesian Optimization via Supervised Dimension ReductionCode0
High-dimensional Bayesian optimization using low-dimensional feature spacesCode0
TensorProjection Layer: A Tensor-Based Dimension Reduction Method in Deep Neural NetworksCode0
Assessing the similarity of real matrices with arbitrary shapeCode0
High-Dimensional Feature Selection for Genomic DatasetsCode0
High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection ApproachCode0
Dimension Reduction with Prior Information for Knowledge DiscoveryCode0
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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