SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1201–1210 of 3304 papers

TitleStatusHype
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Broadcast Product: Shape-aligned Element-wise Multiplication and Beyond—0
Explaining Genetic Programming Trees using Large Language Models—0
Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with Dimensionality Reduction—0
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks—0
Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach—0
Explore intrinsic geometry of sleep dynamics and predict sleep stage by unsupervised learning techniques—0
Exploring Dimensionality Reduction Techniques in Multilingual Transformers—0
An information-geometric approach to feature extraction and moment reconstruction in dynamical systems—0
Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning—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