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 1731–1740 of 3304 papers

TitleStatusHype
Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images—0
Theoretical Understandings of Product Embedding for E-commerce Machine Learning—0
Learning-Augmented Sketches for Hessians—0
Multi-Space Evolutionary Search for Large-Scale Optimization—0
Non-linear, Sparse Dimensionality Reduction via Path Lasso Penalized AutoencodersCode0
Center Smoothing: Certified Robustness for Networks with Structured OutputsCode0
A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution—0
A Deep Embedded Refined Clustering Approach for Breast Cancer Distinction based on DNA Methylation—0
Random Projections for Improved Adversarial Robustness—0
Joint Characterization of Multiscale Information in High Dimensional Data—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