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 1051–1060 of 3304 papers

TitleStatusHype
Cooperative Thresholded Lasso for Sparse Linear Bandit—0
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality ReductionCode0
Parallel Coordinates for Discovery of Interpretable Machine Learning Models—0
On the Noise Sensitivity of the Randomized SVDCode0
Let There Be Order: Rethinking Ordering in Autoregressive Graph GenerationCode0
Classic machine learning methods—0
Decentralized Equalization for Massive MIMO Systems With Colored Noise Samples—0
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations—0
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variabilityCode0
Contrastive inverse regression for dimension reduction—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