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Proceedings Paper

Extending classical multirate signal processing theory to graphs
Author(s): Oguzhan Teke; Palghat P. Vaidyanathan
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Paper Abstract

A variety of different areas consider signals that are defined over graphs. Motivated by the advancements in graph signal processing, this study first reviews some of the recent results on the extension of classical multirate signal processing to graphs. In these results, graphs are allowed to have directed edges. The possibly non-symmetric adjacency matrix A is treated as the graph operator. These results investigate the fundamental concepts for multirate processing of graph signals such as noble identities, aliasing, and perfect reconstruction (PR). It is shown that unless the graph satisfies some conditions, these concepts cannot be extended to graph signals in a simple manner. A structure called M-Block cyclic structure is shown to be sufficient to generalize the results for bipartite graphs on two-channels to M-channel filter banks. Many classical multirate ideas can be extended to graphs due to the unique eigenstructure of M-Block cyclic graphs. For example, the PR condition for filter banks on these graphs is identical to PR in classical theory, which allows the use of well-known filter bank design techniques. In order to utilize these results, the adjacency matrix of an M-Block cyclic graph should be given in the correct permutation. In the final part, this study proposes a spectral technique to identify the hidden M-Block cyclic structure from a graph with noisy edges whose adjacency matrix is given under a random permutation. Numerical simulation results show that the technique can recover the underlying M-Block structure in the presence of random addition and deletion of the edges.

Paper Details

Date Published: 24 August 2017
PDF: 12 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 103941R (24 August 2017); doi: 10.1117/12.2272362
Show Author Affiliations
Oguzhan Teke, California Institute of Technology (United States)
Palghat P. Vaidyanathan, California Institute of Technology (United States)

Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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