SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2601–2610 of 3304 papers

TitleStatusHype
On the Needs for Rotations in Hypercubic Quantization Hashing—0
Convex Formulations for Fair Principal Component AnalysisCode0
Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks—0
UMAP: Uniform Manifold Approximation and Projection for Dimension ReductionCode1
Outlier Detection for Robust Multi-dimensional Scaling—0
Dimension Reduction Using Active Manifolds—0
A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification—0
Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain FeaturesCode0
Rigorous Restricted Isometry Property of Low-Dimensional Subspaces—0
Nonlinear Dimensionality Reduction on Graphs—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