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 2211–2220 of 3304 papers

TitleStatusHype
Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids—0
Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction—0
Learning Image Derived PDE-Phenotypes from fMRI Data—0
Learning Interaction Variables and Kernels from Observations of Agent-Based Systems—0
Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds—0
Learning Inward Scaled Hypersphere Embedding: Exploring Projections in Higher Dimensions—0
Learning Isometric Embeddings of Road Networks using Multidimensional Scaling—0
Learning Locality-Constrained Collaborative Representation for Face Recognition—0
Learning low-dimensional dynamics from whole-brain data improves task capture—0
Learning Low-Dimensional Temporal Representations—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