Data clustering as an unsupervised method has been one of the main attention-grabbing techniques and a large class of tasks can be formulated by this method. Mixture models as a branch of clustering methods have been used in various fields of research such as computer vision and pattern recognition. To apply these models, we need to address some problems such as finding a proper distribution that properly fits data, defining model complexity and estimating the model parameters. In this paper, we apply scaled Dirichlet distribution to tackle the first challenge and propose a novel online variational method to mitigate the other two issues simultaneously. The effectiveness of the proposed work is evaluated by four challenging real applications, namely, text and image spam categorization, diabetes and hepatitis detection.

中文翻译：

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比例Dirichlet分布的Dirichlet过程混合物的在线变分学习

数据聚类作为一种无监督的方法已经成为吸引人们注意的主要技术之一，并且可以用这种方法来制定大量的任务。混合模型作为聚类方法的一个分支，已在各种研究领域中使用，例如计算机视觉和模式识别。要应用这些模型，我们需要解决一些问题，例如找到适合数据的适当分布，定义模型复杂性和估计模型参数。在本文中，我们应用缩放的Dirichlet分布来解决第一个挑战，并提出了一种新颖的在线变分方法来同时缓解其他两个问题。通过四个具有挑战性的实际应用来评估所提议工作的有效性，即文本和图像垃圾邮件分类，糖尿病和肝炎检测。