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A Hyperparameter-Free, Fast and Efficient Framework to Detect Clusters From Limited Samples Based on Ultra High-Dimensional Features

IEEE Access(2022)10.1109/access.2022.3218800Source: DataRank Database

A Hyperparameter-Free, Fast and Efficient Framework to Detect Clusters From Limited Samples Based on Ultra High-Dimensional Features is a research paper published in IEEE Access (2022). On theSindex it has a DataRank of 0.

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)Impact (FWCI 0.00)OA: goldTopics (11)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (3)

Valen E. Johnson ORCID,Suhasini Subba Rao ORCID,Shahina Rahman

Live enrichment

PubMed:37275750 PMC:PMC10237044 OpenAlex:https://openalex.org/W4312712213

National Institute of Health of United States of America

Grant: CA R01 158113

National Science Foundation of United States of America

Grant: DMS-1812054

NCI NIH HHS

Grant: R01 CA158113

FWCI

0.00

Citation Percentile

0.1%

goldLicense: cc-by-nc-nd

journalhttps://ieeexplore.ieee.org/ielx7/6287639/6514899/09934902.pdf

publisherhttps://ieeexplore.ieee.org/ielx7/6287639/6514899/09934902.pdf

publisherhttps://ieeexplore.ieee.org/ielam/6287639/9668973/9934902-aam.pdf

publisherhttp://xplorestaging.ieee.org/ielx7/6287639/9668973/09934902.pdf?arnumber=9934902

journalhttps://doi.org/10.1109/access.2022.3218800

Fields of Study

Domain Adaptation and Few-Shot LearningAdvanced Clustering Algorithms ResearchAdvanced Image and Video Retrieval Techniques

Keywords

Cluster analysisComputer scienceArtificial intelligenceInformation retrievalClusteringHigh-dimensional FeaturesGram MatrixHyperparameter-Free

Sustainable Development Goals

SDG 0: Quality Education