Adaptive Directional Haar Tight Framelets on Bounded Domains for Digraph Signal Representations

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number7
Journal / PublicationJournal of Fourier Analysis and Applications
Volume27
Issue number2
Online published19 Feb 2021
Publication statusPublished - Apr 2021

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