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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
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
Zeitschrift
Journal of Computational Biology
Quellenangaben
Band: 19,
Heft: 11,
Seiten: 1251-1264
Verlag
Mary Ann Liebert
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Biomathematics and Biometry (IBB)