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 1311–1320 of 3304 papers

TitleStatusHype
Non-Linear Spectral Dimensionality Reduction Under Uncertainty—0
Reducing Redundancy in the Bottleneck Representation of the Autoencoders—0
Distributionally Robust Fair Principal Components via Geodesic Descents—0
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender SystemsCode0
Grassmann Stein Variational Gradient DescentCode0
A comprehensive survey on computational learning methods for analysis of gene expression data—0
TTS-GAN: A Transformer-based Time-Series Generative Adversarial NetworkCode2
A Comparison of Representation Learning Methods for Dimensionality Reduction of fMRI Scans for Classification of ADHD—0
On Manifold Hypothesis: Hypersurface Submanifold Embedding Using Osculating Hyperspheres—0
A selective review of sufficient dimension reduction for multivariate response regression—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