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 511–520 of 3304 papers

TitleStatusHype
0-dimensional Homology Preserving Dimensionality Reduction with TopoMap—0
Applying Ricci Flow to High Dimensional Manifold Learning—0
Applying Graph-based Keyword Extraction to Document Retrieval—0
A hierarchical approach with feature selection for emotion recognition from speech—0
A Heath-Jarrow-Morton framework for energy markets: a pragmatic approach—0
Applying a random projection algorithm to optimize machine learning model for breast lesion classification—0
Accelerated Canonical Polyadic Decomposition by Using Mode Reduction—0
Thermodynamically Consistent Latent Dynamics Identification for Parametric Systems—0
Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning—0
Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size 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