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EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis
Abstract : 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.
Cited literature [36 references]
https://hal.inria.fr/hal-01261374
Contributor : Team Perception <>
Submitted on : Monday, January 25, 2016 - 11:28:54 AM
Last modification on : Thursday, July 9, 2020 - 9:44:38 AM
Document(s) archivé(s) le : Tuesday, April 26, 2016 - 10:50:48 AM
Contributor : Team Perception <>
Submitted on : Monday, January 25, 2016 - 11:28:54 AM
Last modification on : Thursday, July 9, 2020 - 9:44:38 AM
Document(s) archivé(s) le : Tuesday, April 26, 2016 - 10:50:48 AM
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Gebru-11dec2015.pdf
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