Accession Number : AD1048429

Title :   Signal Processing for Time-Series Functions on a Graph

Descriptive Note : Technical Report,01 Jun 2017,30 Sep 2017

Corporate Author : US Army Research Laboratory Aberdeen Proving Ground United States

Personal Author(s) : Muoz-Barona,Humberto ; Vettel,Jean ; Bohannon,Addison

Full Text :

Report Date : 01 Feb 2018

Pagination or Media Count : 28

Abstract : Previous research introduced signal processing on graphs, an approach to generalize signal processing tools such as filtering to functions supported on graphs. These methods can be applied to scalar functions with a domain that can be described by a fixed weighted undirected graph. We consider here time-series functions supported on a fixed, weighted, undirected graph and show that an extension to the approach of Shuman et al. does not generalize to this problem, but rather suffers from a catastrophic loss of temporal information in the signal during convolution operations. Finally, we propose alternative signal processing approaches to time-series functions on a fixed graph.

Descriptors :   signal processing , MACHINE LEARNING , graph theory , network science , scalar functions , time series analysis , fourier analysis , weighting functions

Subject Categories : Theoretical Mathematics

Distribution Statement : APPROVED FOR PUBLIC RELEASE