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Maximum likelihood based clustering via parallel computing

Alessandro Barilli

Maximum likelihood based clustering via parallel computing.

Rel. Mauro Gasparini. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Matematica, 2019

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Abstract:

The following pages contain a review of some recent and ongoing work on model-based clustering, in particular hard and soft assignment. It is analysed up to which point, with modern tools of optimization and parallel computing, it is possible to use basic methods such as maximum likelihood and hard assignment towards automatic identification of the classes and of the class labels of the sampled subjects. The standard soft classification approach using the EM (expectation maximization) algorithm will be compared to the hard assignment approach using maximum likelihood. The latter's limits will be analysed at a computational level, under and without the presence of hypotheses of local independence. Limits and applications of such algorithms to real datasets will be shown.

Relators: Mauro Gasparini
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 105
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Matematica
Classe di laurea: New organization > Master science > LM-44 - MATHEMATICAL MODELLING FOR ENGINEERING
Ente in cotutela: Aalto University (FINLANDIA)
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/12728
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