SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2841–2850 of 3304 papers

TitleStatusHype
Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design—0
BDEC:Brain Deep Embedded Clustering model—0
Beam-Space MIMO Radar for Joint Communication and Sensing with OTFS Modulation—0
Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction—0
Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans—0
BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction—0
Simultaneous Best Subset Selection and Dimension Reduction via Primal-Dual Iterations—0
Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs—0
BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractor—0
Beyond Worst-Case Dimensionality Reduction for Sparse Vectors—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