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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Constantinopoulos, C. | en |
| dc.contributor.author | Likas, A. | en |
| dc.date.accessioned | 2015-11-24T17:01:29Z | - |
| dc.date.available | 2015-11-24T17:01:29Z | - |
| dc.identifier.issn | 1045-9227 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10933 | - |
| dc.rights | Default Licence | - |
| dc.subject | clustering | en |
| dc.subject | mixture models | en |
| dc.subject | model selection | en |
| dc.subject | variational bayes methods | en |
| dc.subject | model selection | en |
| dc.subject | likelihood | en |
| dc.subject | algorithm | en |
| dc.subject | network | en |
| dc.subject | em | en |
| dc.title | Unsupervised learning of Gaussian mixtures based on variational component splitting | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | Doi 10.1109/Tnn.2006.891114 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2007 | - |
| heal.abstract | In this paper, we present an incremental method for model selection and learning of Gaussian mixtures based on the recently proposed variational Bayes approach. The method adds components to the mixture using a Bayesian splitting test procedure: a component is split into two components and then variational update equations are applied only to the parameters of the two components. As a result, either both components are retained in the model or one of them is found to be redundant and is eliminated from the model. In our approach, the model selection problem is treated locally, in a region of the data space, so we can set more informative priors based on the local data distribution. A modified Bayesian mixture model is presented to implement this approach, along with a learning algorithm that iteratively applies a splitting test on each mixture component. Experimental results and comparisons with two other techniques testify for the adequacy of the proposed approach. | en |
| heal.journalName | Ieee Transactions on Neural Networks | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Likas-2007-Unsupervised learning of Gaussian mixtures based on variational component splitting.pdf | 1.24 MB | Adobe PDF | View/Open Request a copy |
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