Please use this identifier to cite or link to this item:
https://olympias.lib.uoi.gr/jspui/handle/123456789/11049Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Tzortzis, G. F. | en |
| dc.contributor.author | Likas, A. C. | en |
| dc.date.accessioned | 2015-11-24T17:02:24Z | - |
| dc.date.available | 2015-11-24T17:02:24Z | - |
| dc.identifier.issn | 1045-9227 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/11049 | - |
| dc.rights | Default Licence | - |
| dc.subject | clustering | en |
| dc.subject | mixture models | en |
| dc.subject | multiview learning | en |
| dc.title | Multiple View Clustering Using a Weighted Combination of Exemplar-Based Mixture Models | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.identifier.primary | Doi 10.1109/Tnn.2010.2081999 | - |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2010 | - |
| heal.abstract | Multiview clustering partitions a dataset into groups by simultaneously considering multiple representations (views) for the same instances. Hence, the information available in all views is exploited and this may substantially improve the clustering result obtained by using a single representation. Usually, in multiview algorithms all views are considered equally important, something that may lead to bad cluster assignments if a view is of poor quality. To deal with this problem, we propose a method that is built upon exemplar-based mixture models, called convex mixture models (CMMs). More specifically, we present a multiview clustering algorithm, based on training a weighted multiview CMM, that associates a weight with each view and learns these weights automatically. Our approach is computationally efficient and easy to implement, involving simple iterative computations. Experiments with several datasets confirm the advantages of assigning weights to the views and the superiority of our framework over single-view and unweighted multiview CMMs, as well as over another multiview algorithm which is based on kernel canonical correlation analysis. | 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-2010-Multiple View Clustering Using a Weighted Combination of Exemplar-Based Mixture Models.pdf | 437.8 kB | Adobe PDF | View/Open Request a copy |
This item is licensed under a Creative Commons License
