SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2261–2270 of 3304 papers

TitleStatusHype
Linearized Wasserstein dimensionality reduction with approximation guarantees—0
Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction—0
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes—0
Linear normalised hash function for clustering gene sequences and identifying reference sequences from multiple sequence alignments—0
Linear Tensor Projection Revealing Nonlinearity—0
LInKs "Lifting Independent Keypoints" -- Partial Pose Lifting for Occlusion Handling with Improved Accuracy in 2D-3D Human Pose Estimation—0
LiteVR: Interpretable and Lightweight Cybersickness Detection using Explainable AI—0
Local Averaging Accurately Distills Manifold Structure From Noisy Data—0
Local Deep-Feature Alignment for Unsupervised Dimension Reduction—0
Local Fisher Discriminant Analysis for Pedestrian Re-identification—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