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 621–630 of 3304 papers

TitleStatusHype
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks—0
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text SpatializationsCode0
Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemiaCode0
On-the-fly spectral unmixing based on Kalman filtering—0
Dimension-reduced Reconstruction Map Learning for Parameter Estimation in Likelihood-Free Inference Problems—0
Optimal high-precision shadow estimation—0
Word Embedding Dimension Reduction via Weakly-Supervised Feature SelectionCode0
ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion MapCode0
TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional RegressionCode0
Spectral Self-supervised Feature Selection—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