Construction of Tight Frames on Graphs and Application to Denoising
Not on the map. Why?
Abstract (arXiv)
Given a neighborhood graph representation of a finite set of points xi∈ℝd,i=1,ldots,n, we construct a frame (redundant dictionary) for the space of real-valued functions defined on the graph. This frame is adapted to the underlying geometrical structure of the xi, has finitely many elements, and these elements are localized in frequency as well as in space. This construction follows the ideas of Hammond et al. (2011), with the key point that we construct a tight (or Parseval) frame. This means we have a very simple, explicit reconstruction formula for every function f defined on the graph from the coefficients given by its scalar product with the frame elements. We use this representation in the setting of denoising where we are given noisy observations of a function f defined on the graph. By applying a thresholding method to the coefficients in the reconstruction formula, we define an estimate of f whose risk satisfies a tight oracle inequality.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Aug 2014.
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Citing papers per year: 2021: 2; 2022: 3.
5 papers.
- 2022Graph Filters for Signal Processing and Machine Learning on Graphscited by 53
- 2021Topologically penalized regression on manifoldscited by 5
- 2021Multivariate Trend Filtering for Lattice Datacited by 4
- 2022Frames for Graph Signals on the Symmetric Group: A Representation Theoretic Approachcited by 0
- 2022Large Graph Signal Denoising with Application to Differential Privacycited by 0
- 2022Graph Filters for Signal Processing and Machine Learning on Graphs16 Nov 2022
- 2022Large Graph Signal Denoising with Application to Differential Privacy5 Sep 2022
- 2022Frames for Graph Signals on the Symmetric Group: A Representation Theoretic Approach6 Mar 2022
- 2021Multivariate Trend Filtering for Lattice Data29 Dec 2021
- 2021Topologically penalized regression on manifolds26 Oct 2021