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
Model-free Nonconvex Matrix Completion: Local Minima Analysis and Applications in Memory-efficient Kernel PCA—0
Memory-efficient training with streaming dimensionality reduction—0
Merging SVMs with Linear Discriminant Analysis: A Combined Model—0
Merging Two Cultures: Deep and Statistical Learning—0
Message-Relevant Dimension Reduction of Neural Populations—0
MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails—0
Metastatic Breast Cancer Prognostication Through Multimodal Integration of Dimensionality Reduction Algorithms and Classification Algorithms—0
Metrics for Probabilistic Geometries—0
Microstructure under the Microscope: Tools to Survive and Thrive in The Age of (Too Much) Information—0
MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering—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