SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 821–830 of 3304 papers

TitleStatusHype
MISS: Multiclass Interpretable Scoring SystemsCode0
Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods—0
Fun with Flags: Robust Principal Directions via Flag ManifoldsCode0
Dynamic and Memory-efficient Shape Based Methodologies for User Type Identification in Smart Grid Applications—0
Nonlinear functional regression by functional deep neural network with kernel embedding—0
Scalable manifold learning by uniform landmark sampling and constrained locally linear embedding—0
Inference and Visualization of Community Structure in Attributed Hypergraphs Using Mixed-Membership Stochastic Block ModelsCode0
A Novel method for Schizophrenia classification using nonlinear features and neural networks—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
Agnostically Learning Multi-index Models with Queries—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