A Parametric Classification Rule Based on the Exponentially Embedded Family
A Parametric Classification Rule Based on the Exponentially Embedded Family
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DOI：
10.1109/tnnls.2014.2383692
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发表时间：
2015-01
影响因子：
10.4
通讯作者：
Bo Tang;Haibo He;Quan Ding;S. Kay
中科院分区：
文献类型：
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作者：
Bo Tang;Haibo He;Quan Ding;S. Kay
在本文中，我们扩展的指数嵌入的家庭（EEF），一种新的方法，模型的阶估计和概率密度函数的建设最初提出的Kay在2005年，多变量模式识别。具体而言，参数分类器规则的基础上的EEF的开发，在其中，我们构造一个分布为每个类的基础上的参考分布。所提出的方法可以解决不同类型的分类问题，无论是数据驱动的方式或模型驱动的方式。在本文中，我们证明了它的有效性与合成数据分类和现实生活中的数据驱动的方式和电能质量扰动分类的例子，在模型驱动的方式分类的例子。为了评估我们的方法的分类性能，蒙特-卡罗方法在我们的实验中使用。实验结果表明，该方法具有广泛的应用前景.
In this paper, we extend the exponentially embedded family (EEF), a new approach to model order estimation and probability density function construction originally proposed by Kay in 2005, to multivariate pattern recognition. Specifically, a parametric classifier rule based on the EEF is developed, in which we construct a distribution for each class based on a reference distribution. The proposed method can address different types of classification problems in either a data-driven manner or a model-driven manner. In this paper, we demonstrate its effectiveness with examples of synthetic data classification and real-life data classification in a data-driven manner and the example of power quality disturbance classification in a model-driven manner. To evaluate the classification performance of our approach, the Monte-Carlo method is used in our experiments. The promising experimental results indicate many potential applications of the proposed method.