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 461–470 of 3304 papers

TitleStatusHype
Performance Examination of Symbolic Aggregate Approximation in IoT Applications—0
NUTS, NARS, and Speech—0
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE—0
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances—0
Canonical Variates in Wasserstein Metric Space—0
Embedding Compression for Efficient Re-Identification—0
Rank Reduction Autoencoders—0
A Survey on Design-space Dimensionality Reduction Methods for Shape Optimization—0
A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics—0
Input Guided Multiple Deconstruction Single Reconstruction neural network models for Matrix Factorization—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