SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 291–300 of 3304 papers

TitleStatusHype
Enhancing Graph Attention Neural Network Performance for Marijuana Consumption Classification through Large-scale Augmented Granger Causality (lsAGC) Analysis of Functional MR Images—0
Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation—0
Doubly Non-Central Beta Matrix Factorization for Stable Dimensionality Reduction of Bounded Support Matrix Data—0
Enhancing literature review with LLM and NLP methods. Algorithmic trading case—0
Simultaneous Dimensionality Reduction for Extracting Useful Representations of Large Empirical Multimodal Datasets—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins—0
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation—0
Learning signals defined on graphs with optimal transport and Gaussian process regression—0
A Kernelization-Based Approach to Nonparametric Binary Choice 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