SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2351–2360 of 3304 papers

TitleStatusHype
Unsupervised feature selection algorithm framework based on neighborhood interval disturbance fusion—0
Unsupervised Feature Selection Based on the Morisita Estimator of Intrinsic Dimension—0
Unsupervised Feature Selection via Multi-step Markov Transition Probability—0
Unsupervised Hashtag Retrieval and Visualization for Crisis Informatics—0
Unsupervised Kernel Dimension Reduction—0
Unsupervised Learning: Comparative Analysis of Clustering Techniques on High-Dimensional Data—0
Unsupervised Learning for Fault Detection of HVAC Systems: An OPTICS -based Approach for Terminal Air Handling Units—0
Unsupervised Learning for Topological Classification of Transportation Networks—0
Unsupervised learning of Data-driven Facial Expression Coding System (DFECS) using keypoint tracking—0
Unsupervised low-rank representations for speech emotion recognition—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