SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 131–140 of 3304 papers

TitleStatusHype
An efficient aggregation method for the symbolic representation of temporal dataCode1
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction ModelsCode1
A hyperparameter-tuning approach to automated inverse planningCode1
A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithmsCode1
A local approach to parameter space reduction for regression and classification tasksCode1
A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiologyCode1
ATD: Augmenting CP Tensor Decomposition by Self SupervisionCode1
Algorithmic Stability and Generalization of an Unsupervised Feature Selection AlgorithmCode1
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximationsCode1
A Hybrid Architecture for Out of Domain Intent Detection and Intent DiscoveryCode1
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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