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 1911–1920 of 3304 papers

TitleStatusHype
Rehabilitating Isomap: Euclidean Representation of Geodesic Structure—0
Rehabilitation of Count-based Models for Word Vector Representations—0
Improving Surrogate Model Robustness to Perturbations for Dynamical Systems Through Machine Learning and Data Assimilation—0
Relation between PLS and OLS regression in terms of the eigenvalue distribution of the regressor covariance matrix—0
Parameter-wise co-clustering for high-dimensional data—0
Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration—0
Relevance for Human Robot Collaboration—0
Reliable Distributed Clustering with Redundant Data Assignment—0
Representational Analysis of Binding in Language Models—0
Representation-Constrained Autoencoders and an Application to Wireless Positioning—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