SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1221–1230 of 3304 papers

TitleStatusHype
A Review, Framework and R toolkit for Exploring, Evaluating, and Comparing Visualizations—0
A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection—0
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems—0
2D+3D facial expression recognition via embedded tensor manifold regularization—0
Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices—0
Co-regularized Multi-view Sparse Reconstruction Embedding for Dimension Reduction—0
Deep Compressed Learning for 3D Seismic Inversion—0
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Deep Autoencoders for Dimensionality Reduction of High-Content Screening 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