SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1481–1490 of 3304 papers

TitleStatusHype
Robust factored principal component analysis for matrix-valued outlier accommodation and detection—0
Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay—0
Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization—0
A Cross Entropy test allows quantitative statistical comparison of t-SNE and UMAP representations—0
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning ModelsCode0
Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design—0
Joint Characterization of the Cryospheric Spectral Feature Space—0
Dimensionality Reduction for Categorical Data—0
CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data—0
Data-independent Low-complexity KLT Approximations for Image and Video Coding—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