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Chui, C.K.* ; Filbir, F. ; Mhaskar, H.N.*

Representation of functions on big data: Graphs and trees.

Appl. Comput. Harmon. Anal. 38, 489-509 (2014)
Verlagsversion DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Many current problems dealing with big data can be cast efficiently as function approximation on graphs. The information in the graph structure can often be reorganized in the form of a tree; for example, using clustering techniques. The objective of this paper is to develop a new system of orthogonal functions on weighted trees. The system is local, easily implementable, and allows for scalable approximations without saturation. A novelty of our orthogonal system is that the Fourier projections are uniformly bounded in the supremum norm. We describe in detail a construction of wavelet-like representations and estimate the degree of approximation of functions on the trees.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Analysis On Graphs And Trees ; Big Data ; Function Approximation On Big Data ; Wavelet-like Representation; Diffusion Maps; Laplacian; Wavelets; Frames
ISSN (print) / ISBN 1063-5203
e-ISSN 1096-603X
Quellenangaben Band: 38, Heft: 3, Seiten: 489-509 Artikelnummer: , Supplement: ,
Verlag Academic Press
Verlagsort San Diego, Calif. [u.a.]
Begutachtungsstatus