SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2641–2650 of 3304 papers

TitleStatusHype
Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning—0
Extreme Dimension Reduction for Handling Covariate Shift—0
Wisdom of the crowd from unsupervised dimension reduction—0
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery—0
Random matrix approach to estimation of high-dimensional factor models—0
Practical Hash Functions for Similarity Estimation and Dimensionality ReductionCode0
Universality of macroscopic neuronal dynamics in Caenorhabditis elegans—0
Positive semi-definite embedding for dimensionality reduction and out-of-sample extensionsCode0
"I know it when I see it". Visualization and Intuitive Interpretability—0
Deep Gaussian Mixture Models—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