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Open Access Green as soon as Postprint is submitted to ZB.
Locally learning biomedical data using diffusion frames.
J. Comput. Biol. 19, 1251-1264 (2012)
Diffusion geometry techniques are useful to classify patterns and visualize high-dimensional datasets. Building upon ideas from diffusion geometry, we outline our mathematical foundations for learning a function on high-dimension biomedical data in a local fashion from training data. Our approach is based on a localized summation kernel, and we verify the computational performance by means of exact approximation rates. After these theoretical results, we apply our scheme to learn early disease stages in standard and new biomedical datasets.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Graphs And Networks ; Machine Learning; Nonlinear Dimensionality Reduction ; Macular Degeneration ; Geometric Diffusions ; Structure Definition ; Harmonic-analysis ; Laplacian ; Sphere ; Representation ; Eigenfunctions ; Diagnosis
ISSN (print) / ISBN
1066-5277
e-ISSN
1557-8666
Journal
Journal of Computational Biology
Quellenangaben
Volume: 19,
Issue: 11,
Pages: 1251-1264
Publisher
Mary Ann Liebert
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Biomathematics and Biometry (IBB)