Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.521948
Title: Visualisation of structured data through generative probabilistic modeling
Author: Gianniotis, Nikolaos
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2008
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Abstract:
This thesis is concerned with the construction of topographic maps of structured data. A probabilistic generative model-based approach is taken, inspired by the GTM algorithm. De- pending on the data at hand, the form of a probabilistic generative model is specified that is appropriate for modelling the probability density of the data. A mixture of such models is formulated which is topographically constrained on a low-dimensional latent space. By con- strained, we mean that each point in the latent space determines the parameters of one model via a smooth non-linear mapping; by topographic, we mean that neighbouring latent points gen- erate similar parameters which address statistically similar models. The constrained mixture is trained to model the density of the structured data. A map is constructed by projecting each data item to a location on the latent space where the local latent points are associated with models that express a high probability of having generated the particular data item. We present three formulations for constructing topographic maps of structured data. Two of them are concerned with tree-structured data and employ hidden Markov trees and Markov trees as probabilistic generative models. The third approach is concerned with astronomical light curves from eclipsing binary stars and employs a physical-based model. The formulation of the all three models is accompanied by experiments and analysis of the resulting topographic maps.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.521948  DOI: Not available
Keywords: QA75 Electronic computers. Computer science
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