SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 11–20 of 3304 papers

TitleStatusHype
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Demonstrating Superresolution in Radar Range Estimation Using a Denoising Autoencoder—0
Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises—0
FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-ResolutionCode0
Let the Tree Decide: FABART A Non-Parametric Factor Model—0
On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiologic boundary conditions—0
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt PerspectiveCode0
Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material—0
Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction—0
Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace 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