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Ehler, M. ; Filbir, F. ; Mhaskar, H.N.*

Locally learning biomedical data using diffusion frames.

J. Comput. Biol. 19, 1251-1264 (2012)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
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
Corresponding Author
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
Quellenangaben Volume: 19, Issue: 11, Pages: 1251-1264 Article Number: , Supplement: ,
Publisher Mary Ann Liebert
Non-patent literature Publications
Reviewing status Peer reviewed