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.
| Original language | English |
|---|---|
| Article number | 7 |
| Journal | Journal of Fourier Analysis and Applications |
| Volume | 27 |
| Issue number | 2 |
| Online published | 19 Feb 2021 |
| DOIs | |
| Publication status | Published - Apr 2021 |
Research Keywords
- Adaptive systems
- Bounded domains
- Coarse-grained chain
- Deep learning
- Digraph signal
- Directional Haar tight framelets
- Graph clustering
- Graph signal processing
- Machine learning
- Network
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Dive into the research topics of 'Adaptive Directional Haar Tight Framelets on Bounded Domains for Digraph Signal Representations'. Together they form a unique fingerprint.Projects
- 1 Finished
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GRF: Framelets on Graphs for Deep Learning Applications
ZHUANG, X. (Principal Investigator / Project Coordinator)
1/01/20 → 7/12/23
Project: Research
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