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 2131–2140 of 3304 papers

TitleStatusHype
LEt-SNE: A Hybrid Approach To Data Embedding and Visualization of Hyperspectral ImageryCode0
A Topological "Reading" Lesson: Classification of MNIST using TDA—0
Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data—0
Multiclass spectral feature scaling method for dimensionality reduction—0
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models—0
Earthmover-based manifold learning for analyzing molecular conformation spacesCode0
Roweis Discriminant Analysis: A Generalized Subspace Learning MethodCode0
Efficient Sketching Algorithm for Sparse Binary Data—0
A Multi-view Dimensionality Reduction Algorithm Based on Smooth Representation Model—0
Hierarchical stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries—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