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231224s2016 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2016.2522425
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|a eng
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| 100 |
1 |
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|a Gebru, Israel Dejene
|e verfasserin
|4 aut
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| 245 |
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|a EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis
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| 264 |
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|c 2016
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 12.06.2017
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|a Date Revised 08.09.2017
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Data clustering has received a lot of attention and numerous methods, algorithms and software packages are available. Among these techniques, parametric finite-mixture models play a central role due to their interesting mathematical properties and to the existence of maximum-likelihood estimators based on expectation-maximization (EM). In this paper we propose a new mixture model that associates a weight with each observed point. We introduce the weighted-data Gaussian mixture and we derive two EM algorithms. The first one considers a fixed weight for each observation. The second one treats each weight as a random variable following a gamma distribution. We propose a model selection method based on a minimum message length criterion, provide a weight initialization strategy, and validate the proposed algorithms by comparing them with several state of the art parametric and non-parametric clustering techniques. We also demonstrate the effectiveness and robustness of the proposed clustering technique in the presence of heterogeneous data, namely audio-visual scene analysis
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|a Journal Article
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|a Alameda-Pineda, Xavier
|e verfasserin
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| 700 |
1 |
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|a Forbes, Florence
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Horaud, Radu
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 38(2016), 12 vom: 08. Dez., Seite 2402-2415
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|x 1939-3539
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|u http://dx.doi.org/10.1109/TPAMI.2016.2522425
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