Unsupervised learning of finite mixture models
Unsupervised learning of finite mixture models
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DOI：
10.1109/34.990138
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发表时间：
2002-03-01
影响因子：
23.6
通讯作者：
Jain, AK
中科院分区：
文献类型：
--
作者：
Figueiredo, MAT;Jain, AK
提出了一种从多元数据中学习有限混合模型的无监督算法。该算法的两个特性证明了“无监督”这个形容词的合理性：1)它能够选择组件的数量；2)与标准的期望最大化（EM）算法不同，它不需要仔细的初始化。所提出的方法还避免了EM用于混合拟合的另一个缺点：在参数空间边界向奇异估计收敛的可能性。我们方法的新颖之处在于，我们不使用模型选择标准来从一组预估计的候选模型中选择一个；相反，我们在单一算法中无缝地集成了估计和模型选择。我们的技术可以应用于任何类型的参数混合模型，它可以编写EM算法；在本文中，我们用涉及高斯混合的实验来说明它。这些实验证明了我们的方法的良好性能。
This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective "unsupervised" is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectation-maximization (EM) algorithm, it does not require careful initialization. The proposed method also avoids another drawback of EM for mixture fitting: the possibility of convergence toward a singular estimate at the boundary of the parameter space. The novelty of our approach is that we do not use a model selection criterion to choose one among a set of preestimated candidate models; instead, we seamlessly integrate estimation and model selection in a single algorithm. Our technique can be applied to any type of parametric mixture model for which it is possible to write an EM algorithm; in this paper, we illustrate it with experiments involving Gaussian mixtures. These experiments testify for the good performance of our approach.