VA + Embeddings STAR: A State-of-the-Art Report on the Use of Embeddings in Visual Analytics

Simple item page
dc.contributor.authorHuang, Zeyangen_US
dc.contributor.authorWitschard, Danielen_US
dc.contributor.authorKucher, Kostiantynen_US
dc.contributor.authorKerren, Andreasen_US
dc.contributor.editorBruckner, Stefanen_US
dc.contributor.editorRaidou, Renata G.en_US
dc.contributor.editorTurkay, Cagatayen_US
dc.date.accessioned2023-06-10T06:28:22Z
dc.date.available2023-06-10T06:28:22Z
dc.date.issued2023
dc.description.abstractOver the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term ''embedding'' when describing the computational approach. Within this context, embeddings are usually (relatively) low-dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer-reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023.en_US
dc.description.documenttypestar
dc.description.number3
dc.description.sectionheadersVA + AI
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14859
dc.identifier.issn1467-8659
dc.identifier.pages539-571
dc.identifier.pages33 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14859
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14859
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectKeywords: embedding techniques, distributed representations, visual analytics, visualization ACM CCS: Human-centered computing -> Visual analytics; Human-centered computing -> Information visualization; Human-centered computing -> Visualization systems and tools; Computing methodologies -> Machine learning; Applied computing
dc.subjectembedding techniques
dc.subjectdistributed representations
dc.subjectvisual analytics
dc.subjectvisualization ACM CCS
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectHuman centered computing
dc.subjectInformation visualization
dc.subjectHuman centered computing
dc.subjectVisualization systems and tools
dc.subjectComputing methodologies
dc.subjectMachine learning
dc.subjectApplied computing
dc.titleVA + Embeddings STAR: A State-of-the-Art Report on the Use of Embeddings in Visual Analyticsen_US
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
v42i3pp539-571_cgf14859.pdf
Size:
7.14 MB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
supplementary-material.zip
Size:
2.48 MB
Format:
Zip file
Download
Collections
EuroVisSTAR2023