SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2731–2740 of 3304 papers

TitleStatusHype
Are We Using Autoencoders in a Wrong Way?Code0
Dimension reduction methods, persistent homology and machine learning for EEG signal analysis of Interictal Epileptic DischargesCode0
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal EffectsCode0
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilityCode0
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data?Code0
ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion MapCode0
The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty QuantificationCode0
RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning TasksCode0
Dimensionality Collapse: Optimal Measurement Selection for Low-Error Infinite-Horizon ForecastingCode0
Identifying Selections Operating on HIV-1 Reverse Transcriptase via Uniform Manifold Approximation and ProjectionCode0
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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