SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1691–1700 of 3304 papers

TitleStatusHype
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brainCode1
Unsupervised Imputation of Non-ignorably Missing Data Using Importance-Weighted Autoencoders—0
HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction—0
Multi-view Data Visualisation via Manifold LearningCode0
Generalized Image Reconstruction over T-AlgebraCode0
Multi-point dimensionality reduction to improve projection layout reliability—0
Entangled Kernels -- Beyond Separability—0
Physics-aware, probabilistic model order reduction with guaranteed stability—0
Joint Dimensionality Reduction for Separable Embedding Estimation—0
VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI—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