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 411–420 of 3304 papers

TitleStatusHype
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
ModalChorus: Visual Probing and Alignment of Multi-modal Embeddings via Modal Fusion MapCode0
Word Embedding Dimension Reduction via Weakly-Supervised Feature SelectionCode0
TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional RegressionCode0
Spectral Self-supervised Feature Selection—0
Unveiling the Potential of BERTopic for Multilingual Fake News Analysis -- Use Case: Covid-19—0
Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design—0
Robust Partial Least Squares Using Low Rank and Sparse Decomposition—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