Manifold Visualization via Short Walks

dc.contributor.authorZhao, Yangen_US
dc.contributor.authorTasoulis, Sotiriosen_US
dc.contributor.authorRoos, Teemuen_US
dc.contributor.editorEnrico Bertini and Niklas Elmqvist and Thomas Wischgollen_US
dc.date.accessioned2016-06-09T09:42:27Z
dc.date.available2016-06-09T09:42:27Z
dc.date.issued2016en_US
dc.description.abstractVisualizing low-dimensional non-linear manifolds underlying high-dimensional data is a challenging data analysis problem. Different manifold visualization methods can be characterized by the associated definitions of proximity between highdimensional data points and score functions that lead to different low-dimensional embeddings, preserving different features in the data. The geodesic distance is a popular and well-justified metric. However, it is very hard to approximate reliably from finite samples especially between far apart points. In this paper, we propose a new method called Minimap. The basic idea is to approximate local geodesic distances by shortest paths along a neighborhood graph with an additional penalizing factor based on the number of steps in the path. Embedding the resulting metric by Sammon mapping further enhances the local structures at the expense of long distances that tend to be less reliable. Experiments on real-world benchmarks suggest that Minimap can robustly visualize manifold structures.en_US
dc.description.sectionheadersMultidimensional and Geospatial Visualizationen_US
dc.description.seriesinformationEuroVis 2016 - Short Papersen_US
dc.identifier.doi10.2312/eurovisshort.20161166en_US
dc.identifier.isbn978-3-03868-014-7en_US
dc.identifier.issn-en_US
dc.identifier.pages85-89en_US
dc.identifier.urihttps://doi.org/10.2312/eurovisshort.20161166en_US
dc.identifier.urihttps://diglib.eg.org:443/handle/10
dc.publisherThe Eurographics Associationen_US
dc.subjectI.4.10 [Image Processing and Computer Vision]en_US
dc.subjectImage Representationen_US
dc.subjectMultidimensionalen_US
dc.titleManifold Visualization via Short Walksen_US
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