SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 411–420 of 3304 papers

TitleStatusHype
A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting—0
A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data—0
A survey of dimensionality reduction techniques—0
A survey of dimensionality reduction techniques based on random projection—0
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems—0
A Survey on Archetypal Analysis—0
A Survey on Design-space Dimensionality Reduction Methods for Shape Optimization—0
Approximation Algorithms for Sparse Principal Component Analysis—0
A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images—0
Approximate Matrix Multiplication with Application to Linear Embeddings—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