Accession Number:

ADA622176

Title:

An Algebraic Approach to Inference in Complex Networked Structures

Descriptive Note:

Final performance rept. 1 Apr 2012-31 Mar 2015

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA

Personal Author(s):

Report Date:

2015-07-09

Pagination or Media Count:

14.0

Abstract:

Analysis and processing of very large data sets, or big data, poses a significant challenge. Massive data sets are collected and studied in numerous domains, from engineering sciences to social networks, biomolecular research, commerce, and security. Extracting valuable information from big data requires innovative approaches that efficiently process large amounts of data and utilize their structure. This research project developed a paradigm for large-scale data analysis based on the discrete signal processing DSP on graphs DSPG. DSPG extends signal processing concepts and methodologies from the classical signal processing theory to data indexed by general graphs. We introduced fundamental concepts of DSPG, including graph signals and graph filters, graph Fourier transform, graph frequency, and spectrum ordering that extended their counterparts from classical signal processing theory. Big data analysis presents several challenges to DSPG, in particular, in filtering and frequency analysis of very large data sets. We showed how to analyze these large data sets by considering product graphs as a graph model that helps extend the application of DSPG methods to large data sets through efficient implementations based on parallelization and vectorization. We illustrated the applicability of DSPG with numerous studies that are relevant in applications.

Subject Categories:

  • Information Science
  • Statistics and Probability
  • Computer Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE