SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2381–2390 of 3304 papers

TitleStatusHype
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction—0
Performance prediction of data streams on high-performance architecture—0
Stochastic Approximation Algorithms for Principal Component Analysis—0
Auto-weighted Mutli-view Sparse Reconstructive Embedding—0
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections—0
Active Learning with TensorBoard Projector—0
Trigonometric comparison measure: A feature selection method for text categorization—0
Supervised Multiscale Dimension Reduction for Spatial Interaction Networks—0
Exact Cluster Recovery via Classical Multidimensional Scaling—0
Determining Principal Component Cardinality through the Principle of Minimum Description Length—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