Adaptive Directional Haar Tight Framelets on Bounded Domains for Digraph Signal Representations
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 7 |
| Journal / Publication | Journal of Fourier Analysis and Applications |
| Volume | 27 |
| Issue number | 2 |
| Online published | 19 Feb 2021 |
| Publication status | Published - Apr 2021 |
Link(s)
Abstract
Based on hierarchical partitions, we provide the construction of Haar-type tight framelets on any compact set K ⊆ Rd. In particular, on the unit block [0 , 1]d, such tight framelets can be built to be with adaptivity and directionality. We show that the adaptive directional Haar tight framelet systems can be used for digraph signal representations. Some examples are provided to illustrate results in this paper.
Research Area(s)
- Adaptive systems, Bounded domains, Coarse-grained chain, Deep learning, Digraph signal, Directional Haar tight framelets, Graph clustering, Graph signal processing, Machine learning, Network
Citation Format(s)
Adaptive Directional Haar Tight Framelets on Bounded Domains for Digraph Signal Representations. / Xiao, Yuchen; Zhuang, Xiaosheng.
In: Journal of Fourier Analysis and Applications, Vol. 27, No. 2, 7, 04.2021.
In: Journal of Fourier Analysis and Applications, Vol. 27, No. 2, 7, 04.2021.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
