SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1961–1970 of 3304 papers

TitleStatusHype
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification—0
Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering—0
Grassmann Iterative Linear Discriminant Analysis with Proxy Matrix Optimization—0
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing—0
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction—0
GroupEnc: encoder with group loss for global structure preservation—0
Group Invariant Deep Representations for Image Instance Retrieval—0
Group Preserving Label Embedding for Multi-Label Classification—0
Γ-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data—0
Hand Gesture Recognition with Leap Motion—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