SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2371–2380 of 3304 papers

TitleStatusHype
Active Learning with TensorBoard Projector—0
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections—0
Trigonometric comparison measure: A feature selection method for text categorization—0
Supervised Multiscale Dimension Reduction for Spatial Interaction Networks—0
Determining Principal Component Cardinality through the Principle of Minimum Description Length—0
Exact Cluster Recovery via Classical Multidimensional Scaling—0
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery—0
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization—0
Group Preserving Label Embedding for Multi-Label Classification—0
bigMap: Big Data Mapping with Parallelized t-SNE—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