SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2621–2630 of 3304 papers

TitleStatusHype
Using Deep Autoencoders for Facial Expression Recognition—0
Generalized two-dimensional linear discriminant analysis with regularization—0
Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysis—0
Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer—0
Robust Kronecker Component Analysis—0
Unsupervised Hashtag Retrieval and Visualization for Crisis Informatics—0
Computation of the Maximum Likelihood estimator in low-rank Factor Analysis—0
Semi-supervised Fisher vector network—0
A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA—0
Soft Locality Preserving Map (SLPM) for Facial Expression Recognition—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