PuSH - Publikationsserver des Helmholtz Zentrums München

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

Locally learning biomedical data using diffusion frames.

J. Comput. Biol. 19, 1251-1264 (2012)
Verlagsversion Volltext DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
Weitere Metriken?
Zusatzinfos bearbeiten [➜Einloggen]
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
Quellenangaben Band: 19, Heft: 11, Seiten: 1251-1264 Artikelnummer: , Supplement: ,
Verlag Mary Ann Liebert
Begutachtungsstatus Peer reviewed