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 2081–2090 of 3304 papers

TitleStatusHype
Sparse Generalized Principal Component Analysis for Large-scale Applications beyond Gaussianity—0
Sparse Manifold Clustering and Embedding—0
Sparse Matrix-based Random Projection for Classification—0
Sparse Metric Learning via Smooth Optimization—0
Sparse Modelling for Feature Learning in High Dimensional Data—0
Sparse Models for Machine Learning—0
Sparse Multivariate Factor Regression—0
Sparse PCA with False Discovery Rate Controlled Variable Selection—0
Sparse Reduced Rank Regression With Nonconvex Regularization—0
基於稀疏表示之語者識別 (Sparse Representation Based Speaker Identification) [In Chinese]—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