SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2021–2030 of 3304 papers

TitleStatusHype
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence—0
Dimensionality Reduction of Movement Primitives in Parameter Space—0
Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras—0
Multivariate time-series modeling with generative neural networks—0
Reliable Distributed Clustering with Redundant Data Assignment—0
Dimensionality Reduction and Motion Clustering during Activities of Daily Living: 3, 4, and 7 Degree-of-Freedom Arm Movements—0
t-viSNE: Interactive Assessment and Interpretation of t-SNE ProjectionsCode1
Fair Principal Component Analysis and Filter Design—0
Deep reconstruction of strange attractors from time seriesCode1
Stable Sparse Subspace Embedding for Dimensionality Reduction—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