SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2401–2410 of 3304 papers

TitleStatusHype
Variational Quantum Algorithms for Dimensionality Reduction and Classification—0
Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures—0
varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets—0
Vectorial Dimension Reduction for Tensors Based on Bayesian Inference—0
Vector Space Models for Scientific Document Summarization—0
Vehicles Lane-changing Behavior Detection—0
Virtual Codec Supervised Re-Sampling Network for Image Compression—0
Vision Transformers for Action Recognition: A Survey—0
Vision Transformer with Attention Map Hallucination and FFN Compaction—0
Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction—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