Constrained Dominant Set for Retrieval

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dc.contributor.advisor Pelillo, Marcello it_IT
dc.contributor.author Alemu, Leulseged <1991> it_IT
dc.date.accessioned 2016-06-15 it_IT
dc.date.accessioned 2016-10-07T07:58:44Z
dc.date.issued 2016-06-30 it_IT
dc.identifier.uri http://hdl.handle.net/10579/8670
dc.description.abstract Learning new global relations based on an initial affinity of the database objects has shown significant improvements in similarity retrievals. Locally constrained diffusion process is one of the recent effective tools in learning the intrinsic manifold structure of a given data. Existing methods, which constrained the diffusion process locally, have problems - manual choice of optimal local neighborhood size, do not allow for intrinsic relation among the neighbors, fix initialization vector to extract dense neighbor - which negatively affect the affinity propagation. We propose a new approach, which alleviate these issues, based on some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. In particular, we show that by properly controlling a regularization parameter which determines the structure and the scale of the underlying problem, we are in a position to extract dominant set cluster which is constrained to contain user-provided query. Experimental results on standard benchmark datasets show the effectiveness of the proposed approach. it_IT
dc.language.iso it_IT
dc.publisher Università Ca' Foscari Venezia it_IT
dc.rights © Leulseged Alemu, 2016 it_IT
dc.title Constrained Dominant Set for Retrieval it_IT
dc.title.alternative it_IT
dc.type Bachelor Thesis it_IT
dc.degree.name Informatica - computer science it_IT
dc.degree.level Laurea magistrale it_IT
dc.degree.grantor Dipartimento di Scienze Ambientali, Informatica e Statistica it_IT
dc.description.academicyear 2015/2016, sessione estiva it_IT
dc.rights.accessrights closedAccess it_IT
dc.thesis.matricno 854883 it_IT
dc.subject.miur it_IT
dc.description.note it_IT
dc.degree.discipline it_IT
dc.contributor.co-advisor it_IT
dc.date.embargoend 10000-01-01
dc.provenance.upload Leulseged Alemu (854883@stud.unive.it), 2016-06-15 it_IT
dc.provenance.plagiarycheck Marcello Pelillo (pelillo@unive.it), 2016-06-27 it_IT


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