SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1001–1010 of 3304 papers

TitleStatusHype
Distance metric learning based on structural neighborhoods for dimensionality reduction and classification performance improvement—0
Bayesian nonparametric Principal Component Analysis—0
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions—0
Classic machine learning methods—0
An Impossibility Theorem for Node Embedding—0
Distributed estimation of principal support vector machines for sufficient dimension reduction—0
Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints—0
Distributed Low-Rank Estimation Based on Joint Iterative Optimization in Wireless Sensor Networks—0
A Flexible Iterative Framework for Consensus Clustering—0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—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