Skip navigation
Skip navigation

A Structured Learning Approach to Attributed Graph Embedding

Zhao, Haifeng; Zhou, Jun; Robles-Kelly, Antonio

Description

In this paper, we describe the use of concepts from structural and statistical pattern recognition for recovering a mapping which can be viewed as an operator on the graph attribute-set. This mapping can be used to embed graphs into spaces where tasks such as categorisation and relational matching can be effected. We depart from concepts in graph theory to introduce mappings as operators over graph spaces. This treatment leads to the recovery of a mapping based upon the graph attributes which...[Show more]

dc.contributor.authorZhao, Haifeng
dc.contributor.authorZhou, Jun
dc.contributor.authorRobles-Kelly, Antonio
dc.coverage.spatialCesme Turkey
dc.date.accessioned2015-12-10T23:00:21Z
dc.date.createdAugust 18-20 2010
dc.identifier.urihttp://hdl.handle.net/1885/61315
dc.description.abstractIn this paper, we describe the use of concepts from structural and statistical pattern recognition for recovering a mapping which can be viewed as an operator on the graph attribute-set. This mapping can be used to embed graphs into spaces where tasks such as categorisation and relational matching can be effected. We depart from concepts in graph theory to introduce mappings as operators over graph spaces. This treatment leads to the recovery of a mapping based upon the graph attributes which is related to the edge-space of the graphs under study. As a result, this mapping is a linear operator over the attribute set which is associated with the graph topology. Here, we employ an optimisation approach whose cost function is related to the target function used in discrete Markov Random Field approaches. Thus, the proposed method provides a link between concepts in graph theory, statistical inference and linear operators. We illustrate the utility of the recovered embedding for shape matching and categorisation on MPEG7 CE-Shape-1 dataset. We also compare our results to those yielded by alternatives.
dc.publisherSpringer
dc.relation.ispartofseriesJoint International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition 2010
dc.sourceProceedings of the Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition (SSPR 2010) and Statistical Techniques in Pattern Recognition (SPR 2010)
dc.subjectKeywords: Attributed graphs; Data sets; Graph topology; Linear operators; Markov Random Fields; Optimisations; Shape matching; Statistical inference; Statistical pattern recognition; Structured learning; Target functions; Graph theory; Mathematical operators; Patte
dc.titleA Structured Learning Approach to Attributed Graph Embedding
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2010
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationu4334215xPUB602
local.type.statusPublished Version
local.contributor.affiliationZhao, Haifeng, Nanjing University of Science and Technology
local.contributor.affiliationZhou, Jun, College of Engineering and Computer Science, ANU
local.contributor.affiliationRobles-Kelly, Antonio, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage70
local.bibliographicCitation.lastpage79
local.identifier.doi10.1007/978-3-642-14980-1_6
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2016-02-24T11:02:13Z
local.identifier.scopusID2-s2.0-77958464378
CollectionsANU Research Publications

Download

File Description SizeFormat Image
01_Zhao_A_Structured_Learning_Approach_2010.pdf159.72 kBAdobe PDF    Request a copy


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator