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.

本文提出了一种新的带权数据高斯混合模型，基于期望最大化算法提出了两种EM算法，考虑每一个观测值的固定权重或随机变量，同时提出了模型选择方法和权重初始化策略，并在音视频情景分析领域证明了该方法的有效性和鲁棒性。

带权重数据聚类的EM算法及其在音视频场景分析中的应用