The use of clustering systems is very important in those real-word applications where an efficient, both accurate and economical, representation of the data to be processed is necessary. When dealing with statistical models, such a problem is usually related to the estimate of their parameters in the Maximum Likelihood context. At this regard, we propose an EM-based algorithm that uses a hierarchical growing approach, based on a given splitting procedure, to determine in an efficient way the parameters of a mixture of Gaussian clusters. The splitting procedure and the determination of the correct number of clusters are based on a scale-based approach, which imitates the human perception of images. Moreover, each cluster is modelled by means of latent variables, which also ensure a local linear dimension reduction of the data being processed.

Scale-Based Clustering with Latent Variables / FRATTALE MASCIOLI, Fabio Massimo; Panella, Massimo; Rizzi, Antonello; Martinelli, Giuseppe. - STAMPA. - II:(2000), pp. 741-744. (Intervento presentato al convegno European Signal Processing Conference tenutosi a Tampere, Finlandia nel 4-8 Settembre 2000).

Scale-Based Clustering with Latent Variables

FRATTALE MASCIOLI, Fabio Massimo;PANELLA, Massimo;RIZZI, Antonello;MARTINELLI, Giuseppe
2000

Abstract

The use of clustering systems is very important in those real-word applications where an efficient, both accurate and economical, representation of the data to be processed is necessary. When dealing with statistical models, such a problem is usually related to the estimate of their parameters in the Maximum Likelihood context. At this regard, we propose an EM-based algorithm that uses a hierarchical growing approach, based on a given splitting procedure, to determine in an efficient way the parameters of a mixture of Gaussian clusters. The splitting procedure and the determination of the correct number of clusters are based on a scale-based approach, which imitates the human perception of images. Moreover, each cluster is modelled by means of latent variables, which also ensure a local linear dimension reduction of the data being processed.
2000
European Signal Processing Conference
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Scale-Based Clustering with Latent Variables / FRATTALE MASCIOLI, Fabio Massimo; Panella, Massimo; Rizzi, Antonello; Martinelli, Giuseppe. - STAMPA. - II:(2000), pp. 741-744. (Intervento presentato al convegno European Signal Processing Conference tenutosi a Tampere, Finlandia nel 4-8 Settembre 2000).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/249432
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