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 981–990 of 3304 papers

TitleStatusHype
DIRESA, a distance-preserving nonlinear dimension reduction technique based on regularized autoencoders—0
DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification—0
An Impossibility Theorem for Node Embedding—0
Discovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Space—0
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
A Flexible Iterative Framework for Consensus Clustering—0
Discovery of Latent Factors in High-dimensional Data Using Tensor Methods—0
Discovery of sustainable energy materials via the machine-learned material space—0
Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication—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